Overview

Dataset statistics

Number of variables62
Number of observations170
Missing cells4104
Missing cells (%)38.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory82.5 KiB
Average record size in memory496.8 B

Variable types

Numeric13
Categorical40
Unsupported9

Alerts

airdate has constant value "2020-12-18" Constant
_embedded.show.externals.tvrage has constant value "34149.0" Constant
_embedded.show.dvdCountry.name has constant value "Russian Federation" Constant
_embedded.show.dvdCountry.code has constant value "RU" Constant
_embedded.show.dvdCountry.timezone has constant value "Asia/Kamchatka" Constant
url has a high cardinality: 170 distinct values High cardinality
name has a high cardinality: 145 distinct values High cardinality
summary has a high cardinality: 66 distinct values High cardinality
_links.self.href has a high cardinality: 170 distinct values High cardinality
_embedded.show.url has a high cardinality: 90 distinct values High cardinality
_embedded.show.name has a high cardinality: 90 distinct values High cardinality
_embedded.show.premiered has a high cardinality: 69 distinct values High cardinality
_embedded.show.officialSite has a high cardinality: 80 distinct values High cardinality
_embedded.show.image.medium has a high cardinality: 84 distinct values High cardinality
_embedded.show.image.original has a high cardinality: 84 distinct values High cardinality
_embedded.show.summary has a high cardinality: 82 distinct values High cardinality
_embedded.show._links.self.href has a high cardinality: 90 distinct values High cardinality
_embedded.show._links.previousepisode.href has a high cardinality: 90 distinct values High cardinality
image.medium has a high cardinality: 79 distinct values High cardinality
image.original has a high cardinality: 79 distinct values High cardinality
id is highly correlated with _embedded.show.id and 2 other fieldsHigh correlation
season is highly correlated with _embedded.show.externals.thetvdbHigh correlation
number is highly correlated with _embedded.show.network.idHigh correlation
runtime is highly correlated with _embedded.show.runtime and 2 other fieldsHigh correlation
rating.average is highly correlated with _embedded.show.rating.average and 1 other fieldsHigh correlation
_embedded.show.id is highly correlated with id and 1 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with rating.average and 1 other fieldsHigh correlation
_embedded.show.weight is highly correlated with id and 1 other fieldsHigh correlation
_embedded.show.webChannel.id is highly correlated with _embedded.show.network.idHigh correlation
_embedded.show.externals.thetvdb is highly correlated with season and 1 other fieldsHigh correlation
_embedded.show.updated is highly correlated with rating.average and 2 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with id and 5 other fieldsHigh correlation
id is highly correlated with _embedded.show.network.idHigh correlation
season is highly correlated with numberHigh correlation
number is highly correlated with season and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded.show.runtime and 2 other fieldsHigh correlation
rating.average is highly correlated with _embedded.show.updatedHigh correlation
_embedded.show.id is highly correlated with _embedded.show.externals.thetvdbHigh correlation
_embedded.show.runtime is highly correlated with number and 2 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with runtime and 2 other fieldsHigh correlation
_embedded.show.weight is highly correlated with _embedded.show.network.idHigh correlation
_embedded.show.webChannel.id is highly correlated with _embedded.show.network.idHigh correlation
_embedded.show.externals.thetvdb is highly correlated with _embedded.show.idHigh correlation
_embedded.show.updated is highly correlated with rating.average and 1 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with id and 5 other fieldsHigh correlation
id is highly correlated with _embedded.show.id and 1 other fieldsHigh correlation
season is highly correlated with _embedded.show.externals.thetvdbHigh correlation
number is highly correlated with _embedded.show.network.idHigh correlation
runtime is highly correlated with _embedded.show.runtime and 2 other fieldsHigh correlation
rating.average is highly correlated with _embedded.show.rating.averageHigh correlation
_embedded.show.id is highly correlated with id and 1 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with rating.averageHigh correlation
_embedded.show.weight is highly correlated with _embedded.show.network.idHigh correlation
_embedded.show.webChannel.id is highly correlated with _embedded.show.network.idHigh correlation
_embedded.show.externals.thetvdb is highly correlated with season and 1 other fieldsHigh correlation
_embedded.show.updated is highly correlated with _embedded.show.network.idHigh correlation
_embedded.show.network.id is highly correlated with id and 5 other fieldsHigh correlation
id is highly correlated with summary and 26 other fieldsHigh correlation
season is highly correlated with number and 21 other fieldsHigh correlation
number is highly correlated with season and 32 other fieldsHigh correlation
type is highly correlated with summary and 15 other fieldsHigh correlation
airtime is highly correlated with number and 29 other fieldsHigh correlation
airstamp is highly correlated with number and 39 other fieldsHigh correlation
runtime is highly correlated with season and 36 other fieldsHigh correlation
summary is highly correlated with id and 37 other fieldsHigh correlation
rating.average is highly correlated with type and 29 other fieldsHigh correlation
_embedded.show.id is highly correlated with id and 37 other fieldsHigh correlation
_embedded.show.url is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show.name is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show.type is highly correlated with id and 38 other fieldsHigh correlation
_embedded.show.language is highly correlated with id and 38 other fieldsHigh correlation
_embedded.show.status is highly correlated with id and 24 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with season and 33 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with season and 37 other fieldsHigh correlation
_embedded.show.premiered is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show.ended is highly correlated with id and 35 other fieldsHigh correlation
_embedded.show.officialSite is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show.schedule.time is highly correlated with number and 31 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with id and 31 other fieldsHigh correlation
_embedded.show.weight is highly correlated with airstamp and 36 other fieldsHigh correlation
_embedded.show.webChannel.id is highly correlated with id and 28 other fieldsHigh correlation
_embedded.show.webChannel.name is highly correlated with id and 36 other fieldsHigh correlation
_embedded.show.webChannel.country.name is highly correlated with id and 32 other fieldsHigh correlation
_embedded.show.webChannel.country.code is highly correlated with id and 32 other fieldsHigh correlation
_embedded.show.webChannel.country.timezone is highly correlated with id and 32 other fieldsHigh correlation
_embedded.show.webChannel.officialSite is highly correlated with id and 33 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with id and 31 other fieldsHigh correlation
_embedded.show.externals.imdb is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show.image.medium is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show.image.original is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show.summary is highly correlated with id and 37 other fieldsHigh correlation
_embedded.show.updated is highly correlated with id and 36 other fieldsHigh correlation
_embedded.show._links.self.href is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show._links.previousepisode.href is highly correlated with id and 42 other fieldsHigh correlation
image.medium is highly correlated with id and 42 other fieldsHigh correlation
image.original is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with number and 25 other fieldsHigh correlation
_embedded.show.network.name is highly correlated with number and 25 other fieldsHigh correlation
_embedded.show.network.country.name is highly correlated with number and 25 other fieldsHigh correlation
_embedded.show.network.country.code is highly correlated with number and 25 other fieldsHigh correlation
_embedded.show.network.country.timezone is highly correlated with number and 25 other fieldsHigh correlation
number has 4 (2.4%) missing values Missing
runtime has 7 (4.1%) missing values Missing
image has 170 (100.0%) missing values Missing
summary has 104 (61.2%) missing values Missing
rating.average has 141 (82.9%) missing values Missing
_embedded.show.language has 2 (1.2%) missing values Missing
_embedded.show.runtime has 60 (35.3%) missing values Missing
_embedded.show.averageRuntime has 4 (2.4%) missing values Missing
_embedded.show.ended has 91 (53.5%) missing values Missing
_embedded.show.officialSite has 27 (15.9%) missing values Missing
_embedded.show.rating.average has 139 (81.8%) missing values Missing
_embedded.show.network has 170 (100.0%) missing values Missing
_embedded.show.webChannel.id has 2 (1.2%) missing values Missing
_embedded.show.webChannel.name has 2 (1.2%) missing values Missing
_embedded.show.webChannel.country.name has 94 (55.3%) missing values Missing
_embedded.show.webChannel.country.code has 94 (55.3%) missing values Missing
_embedded.show.webChannel.country.timezone has 94 (55.3%) missing values Missing
_embedded.show.webChannel.officialSite has 79 (46.5%) missing values Missing
_embedded.show.dvdCountry has 170 (100.0%) missing values Missing
_embedded.show.externals.tvrage has 168 (98.8%) missing values Missing
_embedded.show.externals.thetvdb has 37 (21.8%) missing values Missing
_embedded.show.externals.imdb has 52 (30.6%) missing values Missing
_embedded.show.image.medium has 6 (3.5%) missing values Missing
_embedded.show.image.original has 6 (3.5%) missing values Missing
_embedded.show.summary has 14 (8.2%) missing values Missing
_embedded.show._links.nextepisode.href has 168 (98.8%) missing values Missing
image.medium has 91 (53.5%) missing values Missing
image.original has 91 (53.5%) missing values Missing
_embedded.show.network.id has 166 (97.6%) missing values Missing
_embedded.show.network.name has 166 (97.6%) missing values Missing
_embedded.show.network.country.name has 166 (97.6%) missing values Missing
_embedded.show.network.country.code has 166 (97.6%) missing values Missing
_embedded.show.network.country.timezone has 166 (97.6%) missing values Missing
_embedded.show.network.officialSite has 170 (100.0%) missing values Missing
_embedded.show.webChannel has 170 (100.0%) missing values Missing
_embedded.show.image has 170 (100.0%) missing values Missing
_embedded.show.webChannel.country has 170 (100.0%) missing values Missing
_embedded.show.dvdCountry.name has 169 (99.4%) missing values Missing
_embedded.show.dvdCountry.code has 169 (99.4%) missing values Missing
_embedded.show.dvdCountry.timezone has 169 (99.4%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
summary is uniformly distributed Uniform
_links.self.href is uniformly distributed Uniform
_embedded.show._links.nextepisode.href is uniformly distributed Uniform
image.medium is uniformly distributed Uniform
image.original is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links.self.href has unique values Unique
image is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.genres is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.schedule.days is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.network is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.dvdCountry is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.network.officialSite is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.webChannel is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.image is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.webChannel.country is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-09-05 04:41:29.300302
Analysis finished2022-09-05 04:41:49.307660
Duration20.01 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018738.4
Minimum1910448
Maximum2341526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:49.356660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1910448
5-th percentile1964844.7
Q11982908.75
median1988123.5
Q32039437.75
95-th percentile2159839.9
Maximum2341526
Range431078
Interquartile range (IQR)56529

Descriptive statistics

Standard deviation71048.04306
Coefficient of variation (CV)0.03519427929
Kurtosis3.592951246
Mean2018738.4
Median Absolute Deviation (MAD)11197.5
Skewness1.876446548
Sum343185528
Variance5047824422
MonotonicityNot monotonic
2022-09-04T23:41:49.447112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19681141
 
0.6%
19829201
 
0.6%
19927351
 
0.6%
19676971
 
0.6%
19881201
 
0.6%
19881211
 
0.6%
19881221
 
0.6%
19881231
 
0.6%
19881241
 
0.6%
19993161
 
0.6%
Other values (160)160
94.1%
ValueCountFrequency (%)
19104481
0.6%
19248901
0.6%
19400361
0.6%
19400411
0.6%
19442571
0.6%
19496361
0.6%
19517411
0.6%
19610051
0.6%
19645721
0.6%
19651781
0.6%
ValueCountFrequency (%)
23415261
0.6%
22986611
0.6%
22152361
0.6%
22003041
0.6%
21973081
0.6%
21972861
0.6%
21761351
0.6%
21689971
0.6%
21618281
0.6%
21574101
0.6%

url
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
https://www.tvmaze.com/episodes/1968114/po-sezonu-videodajdzest-seasonvar-6x51-vypusk-305
 
1
https://www.tvmaze.com/episodes/1982920/fjols-til-fjells-1x05-voi-voi
 
1
https://www.tvmaze.com/episodes/1992735/kinderfanger-1x08-the-labyrinth
 
1
https://www.tvmaze.com/episodes/1967697/on-pointe-1x01-getting-in
 
1
https://www.tvmaze.com/episodes/1988120/on-pointe-1x02-casting-competition
 
1
Other values (165)
165 

Length

Max length174
Median length104
Mean length74.71176471
Min length51

Characters and Unicode

Total characters12701
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1968114/po-sezonu-videodajdzest-seasonvar-6x51-vypusk-305
2nd rowhttps://www.tvmaze.com/episodes/1961005/cuma-2x08-seria-14
3rd rowhttps://www.tvmaze.com/episodes/1989253/to-so-sketci-1x07-7-vypusk
4th rowhttps://www.tvmaze.com/episodes/1988013/muzskaa-tema-1x02-seria-2
5th rowhttps://www.tvmaze.com/episodes/2030153/fox-spirit-matchmaker-9x03-episode-124

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1968114/po-sezonu-videodajdzest-seasonvar-6x51-vypusk-3051
 
0.6%
https://www.tvmaze.com/episodes/1982920/fjols-til-fjells-1x05-voi-voi1
 
0.6%
https://www.tvmaze.com/episodes/1992735/kinderfanger-1x08-the-labyrinth1
 
0.6%
https://www.tvmaze.com/episodes/1967697/on-pointe-1x01-getting-in1
 
0.6%
https://www.tvmaze.com/episodes/1988120/on-pointe-1x02-casting-competition1
 
0.6%
https://www.tvmaze.com/episodes/1988121/on-pointe-1x03-practice-makes-perfect1
 
0.6%
https://www.tvmaze.com/episodes/1988122/on-pointe-1x04-sacrifice-support1
 
0.6%
https://www.tvmaze.com/episodes/1988123/on-pointe-1x05-stepping-up1
 
0.6%
https://www.tvmaze.com/episodes/1988124/on-pointe-1x06-showtime1
 
0.6%
https://www.tvmaze.com/episodes/1999316/sobrevolando-1x06-peru-a-trilha-inca1
 
0.6%
Other values (160)160
94.1%

Length

2022-09-04T23:41:49.650567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1968114/po-sezonu-videodajdzest-seasonvar-6x51-vypusk-3051
 
0.6%
https://www.tvmaze.com/episodes/1998583/mr-right-is-here-1x11-episode-111
 
0.6%
https://www.tvmaze.com/episodes/1990238/taqdeer-1x03-ranakhetra1
 
0.6%
https://www.tvmaze.com/episodes/1990237/taqdeer-1x02-radbadal1
 
0.6%
https://www.tvmaze.com/episodes/1989253/to-so-sketci-1x07-7-vypusk1
 
0.6%
https://www.tvmaze.com/episodes/1988013/muzskaa-tema-1x02-seria-21
 
0.6%
https://www.tvmaze.com/episodes/2030153/fox-spirit-matchmaker-9x03-episode-1241
 
0.6%
https://www.tvmaze.com/episodes/1972569/the-wolf-1x27-episode-271
 
0.6%
https://www.tvmaze.com/episodes/1972570/the-wolf-1x28-episode-281
 
0.6%
https://www.tvmaze.com/episodes/1910448/the-founder-of-diabolism-q-1x22-escape1
 
0.6%
Other values (160)160
94.1%

Most occurring characters

ValueCountFrequency (%)
e1077
 
8.5%
/850
 
6.7%
-846
 
6.7%
s808
 
6.4%
t763
 
6.0%
o650
 
5.1%
w576
 
4.5%
a509
 
4.0%
p483
 
3.8%
i473
 
3.7%
Other values (30)5666
44.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8642
68.0%
Decimal Number1853
 
14.6%
Other Punctuation1360
 
10.7%
Dash Punctuation846
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1077
12.5%
s808
 
9.3%
t763
 
8.8%
o650
 
7.5%
w576
 
6.7%
a509
 
5.9%
p483
 
5.6%
i473
 
5.5%
m470
 
5.4%
d400
 
4.6%
Other values (16)2433
28.2%
Decimal Number
ValueCountFrequency (%)
1438
23.6%
0267
14.4%
9228
12.3%
2214
11.5%
8155
 
8.4%
3123
 
6.6%
5111
 
6.0%
4111
 
6.0%
6108
 
5.8%
798
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/850
62.5%
.340
 
25.0%
:170
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-846
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8642
68.0%
Common4059
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1077
12.5%
s808
 
9.3%
t763
 
8.8%
o650
 
7.5%
w576
 
6.7%
a509
 
5.9%
p483
 
5.6%
i473
 
5.5%
m470
 
5.4%
d400
 
4.6%
Other values (16)2433
28.2%
Common
ValueCountFrequency (%)
/850
20.9%
-846
20.8%
1438
10.8%
.340
 
8.4%
0267
 
6.6%
9228
 
5.6%
2214
 
5.3%
:170
 
4.2%
8155
 
3.8%
3123
 
3.0%
Other values (4)428
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII12701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1077
 
8.5%
/850
 
6.7%
-846
 
6.7%
s808
 
6.4%
t763
 
6.0%
o650
 
5.1%
w576
 
4.5%
a509
 
4.0%
p483
 
3.8%
i473
 
3.7%
Other values (30)5666
44.6%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct145
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Episode 11
 
4
Episode 5
 
4
Episode 4
 
4
Episode 2
 
3
Episode 7
 
3
Other values (140)
152 

Length

Max length99
Median length63
Mean length15.19411765
Min length2

Characters and Unicode

Total characters2583
Distinct characters125
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)77.1%

Sample

1st rowВыпуск 305
2nd rowСерия 14
3rd row7 выпуск
4th rowСерия 2
5th rowEpisode 124

Common Values

ValueCountFrequency (%)
Episode 114
 
2.4%
Episode 54
 
2.4%
Episode 44
 
2.4%
Episode 23
 
1.8%
Episode 73
 
1.8%
Episode 123
 
1.8%
Episode 63
 
1.8%
Episode 33
 
1.8%
Happy Hour2
 
1.2%
Episode 182
 
1.2%
Other values (135)139
81.8%

Length

2022-09-04T23:41:49.751740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode49
 
11.2%
the15
 
3.4%
11
 
2.5%
26
 
1.4%
55
 
1.1%
44
 
0.9%
184
 
0.9%
and4
 
0.9%
of4
 
0.9%
14
 
0.9%
Other values (280)331
75.7%

Most occurring characters

ValueCountFrequency (%)
267
 
10.3%
e223
 
8.6%
i151
 
5.8%
o142
 
5.5%
a140
 
5.4%
s125
 
4.8%
r103
 
4.0%
d97
 
3.8%
t92
 
3.6%
p81
 
3.1%
Other values (115)1162
45.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1747
67.6%
Uppercase Letter383
 
14.8%
Space Separator267
 
10.3%
Decimal Number119
 
4.6%
Other Punctuation43
 
1.7%
Dash Punctuation17
 
0.7%
Other Letter2
 
0.1%
Math Symbol1
 
< 0.1%
Initial Punctuation1
 
< 0.1%
Open Punctuation1
 
< 0.1%
Other values (2)2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e223
12.8%
i151
 
8.6%
o142
 
8.1%
a140
 
8.0%
s125
 
7.2%
r103
 
5.9%
d97
 
5.6%
t92
 
5.3%
p81
 
4.6%
n76
 
4.4%
Other values (46)517
29.6%
Uppercase Letter
ValueCountFrequency (%)
E68
17.8%
S28
 
7.3%
T23
 
6.0%
D20
 
5.2%
C20
 
5.2%
P19
 
5.0%
L17
 
4.4%
H17
 
4.4%
R16
 
4.2%
F15
 
3.9%
Other values (31)140
36.6%
Decimal Number
ValueCountFrequency (%)
132
26.9%
221
17.6%
812
 
10.1%
311
 
9.2%
010
 
8.4%
49
 
7.6%
58
 
6.7%
77
 
5.9%
66
 
5.0%
93
 
2.5%
Other Punctuation
ValueCountFrequency (%)
,14
32.6%
#6
14.0%
.5
 
11.6%
:5
 
11.6%
?3
 
7.0%
&3
 
7.0%
!3
 
7.0%
'2
 
4.7%
"2
 
4.7%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
267
100.0%
Dash Punctuation
ValueCountFrequency (%)
-17
100.0%
Math Symbol
ValueCountFrequency (%)
|1
100.0%
Initial Punctuation
ValueCountFrequency (%)
«1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%
Final Punctuation
ValueCountFrequency (%)
»1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1994
77.2%
Common451
 
17.5%
Cyrillic136
 
5.3%
Han2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e223
 
11.2%
i151
 
7.6%
o142
 
7.1%
a140
 
7.0%
s125
 
6.3%
r103
 
5.2%
d97
 
4.9%
t92
 
4.6%
p81
 
4.1%
n76
 
3.8%
Other values (47)764
38.3%
Cyrillic
ValueCountFrequency (%)
и11
 
8.1%
к10
 
7.4%
с10
 
7.4%
е9
 
6.6%
р9
 
6.6%
о8
 
5.9%
а6
 
4.4%
л5
 
3.7%
А5
 
3.7%
п5
 
3.7%
Other values (30)58
42.6%
Common
ValueCountFrequency (%)
267
59.2%
132
 
7.1%
221
 
4.7%
-17
 
3.8%
,14
 
3.1%
812
 
2.7%
311
 
2.4%
010
 
2.2%
49
 
2.0%
58
 
1.8%
Other values (16)50
 
11.1%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2432
94.2%
Cyrillic136
 
5.3%
None13
 
0.5%
CJK2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
267
 
11.0%
e223
 
9.2%
i151
 
6.2%
o142
 
5.8%
a140
 
5.8%
s125
 
5.1%
r103
 
4.2%
d97
 
4.0%
t92
 
3.8%
p81
 
3.3%
Other values (64)1011
41.6%
Cyrillic
ValueCountFrequency (%)
и11
 
8.1%
к10
 
7.4%
с10
 
7.4%
е9
 
6.6%
р9
 
6.6%
о8
 
5.9%
а6
 
4.4%
л5
 
3.7%
А5
 
3.7%
п5
 
3.7%
Other values (30)58
42.6%
None
ValueCountFrequency (%)
ó3
23.1%
ø2
15.4%
å2
15.4%
í1
 
7.7%
ş1
 
7.7%
ä1
 
7.7%
«1
 
7.7%
á1
 
7.7%
»1
 
7.7%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.34117647
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:49.832739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile10
Maximum2020
Range2019
Interquartile range (IQR)1

Descriptive statistics

Standard deviation373.4514711
Coefficient of variation (CV)5.091975464
Kurtosis24.10588476
Mean73.34117647
Median Absolute Deviation (MAD)0
Skewness5.081447278
Sum12468
Variance139466.0013
MonotonicityNot monotonic
2022-09-04T23:41:49.903244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1113
66.5%
217
 
10.0%
612
 
7.1%
48
 
4.7%
37
 
4.1%
20206
 
3.5%
102
 
1.2%
91
 
0.6%
171
 
0.6%
51
 
0.6%
Other values (2)2
 
1.2%
ValueCountFrequency (%)
1113
66.5%
217
 
10.0%
37
 
4.1%
48
 
4.7%
51
 
0.6%
612
 
7.1%
71
 
0.6%
91
 
0.6%
102
 
1.2%
171
 
0.6%
ValueCountFrequency (%)
20206
3.5%
181
 
0.6%
171
 
0.6%
102
 
1.2%
91
 
0.6%
71
 
0.6%
612
7.1%
51
 
0.6%
48
4.7%
37
4.1%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct36
Distinct (%)21.7%
Missing4
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean17.41566265
Minimum1
Maximum345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:49.985968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile51
Maximum345
Range344
Interquartile range (IQR)8

Descriptive statistics

Standard deviation46.79991707
Coefficient of variation (CV)2.687231489
Kurtosis32.86989931
Mean17.41566265
Median Absolute Deviation (MAD)3
Skewness5.589629143
Sum2891
Variance2190.232238
MonotonicityNot monotonic
2022-09-04T23:41:50.068968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
319
11.2%
515
 
8.8%
414
 
8.2%
613
 
7.6%
112
 
7.1%
212
 
7.1%
710
 
5.9%
89
 
5.3%
108
 
4.7%
98
 
4.7%
Other values (26)46
27.1%
ValueCountFrequency (%)
112
7.1%
212
7.1%
319
11.2%
414
8.2%
515
8.8%
613
7.6%
710
5.9%
89
5.3%
98
4.7%
108
4.7%
ValueCountFrequency (%)
3451
0.6%
3101
0.6%
3091
0.6%
1931
0.6%
1551
0.6%
841
0.6%
691
0.6%
641
0.6%
512
1.2%
461
0.6%

type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
regular
166 
significant_special
 
4

Length

Max length19
Median length7
Mean length7.282352941
Min length7

Characters and Unicode

Total characters1238
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular166
97.6%
significant_special4
 
2.4%

Length

2022-09-04T23:41:50.154474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:50.230628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
regular166
97.6%
significant_special4
 
2.4%

Most occurring characters

ValueCountFrequency (%)
r332
26.8%
a174
14.1%
e170
13.7%
g170
13.7%
l170
13.7%
u166
13.4%
i16
 
1.3%
s8
 
0.6%
n8
 
0.6%
c8
 
0.6%
Other values (4)16
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1234
99.7%
Connector Punctuation4
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r332
26.9%
a174
14.1%
e170
13.8%
g170
13.8%
l170
13.8%
u166
13.5%
i16
 
1.3%
s8
 
0.6%
n8
 
0.6%
c8
 
0.6%
Other values (3)12
 
1.0%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1234
99.7%
Common4
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r332
26.9%
a174
14.1%
e170
13.8%
g170
13.8%
l170
13.8%
u166
13.5%
i16
 
1.3%
s8
 
0.6%
n8
 
0.6%
c8
 
0.6%
Other values (3)12
 
1.0%
Common
ValueCountFrequency (%)
_4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r332
26.8%
a174
14.1%
e170
13.7%
g170
13.7%
l170
13.7%
u166
13.4%
i16
 
1.3%
s8
 
0.6%
n8
 
0.6%
c8
 
0.6%
Other values (4)16
 
1.3%

airdate
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2020-12-18
170 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1700
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-18
2nd row2020-12-18
3rd row2020-12-18
4th row2020-12-18
5th row2020-12-18

Common Values

ValueCountFrequency (%)
2020-12-18170
100.0%

Length

2022-09-04T23:41:50.298628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:50.372466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-18170
100.0%

Most occurring characters

ValueCountFrequency (%)
2510
30.0%
0340
20.0%
-340
20.0%
1340
20.0%
8170
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1360
80.0%
Dash Punctuation340
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2510
37.5%
0340
25.0%
1340
25.0%
8170
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-340
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2510
30.0%
0340
20.0%
-340
20.0%
1340
20.0%
8170
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2510
30.0%
0340
20.0%
-340
20.0%
1340
20.0%
8170
 
10.0%

airtime
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
142 
20:00
 
14
12:00
 
5
06:00
 
3
21:00
 
2
Other values (4)
 
4

Length

Max length5
Median length0
Mean length0.8235294118
Min length0

Characters and Unicode

Total characters140
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)2.4%

Sample

1st row
2nd row
3rd row12:00
4th row12:00
5th row

Common Values

ValueCountFrequency (%)
142
83.5%
20:0014
 
8.2%
12:005
 
2.9%
06:003
 
1.8%
21:002
 
1.2%
19:001
 
0.6%
00:001
 
0.6%
18:001
 
0.6%
22:001
 
0.6%

Length

2022-09-04T23:41:50.437467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:50.521594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
20:0014
50.0%
12:005
 
17.9%
06:003
 
10.7%
21:002
 
7.1%
19:001
 
3.6%
00:001
 
3.6%
18:001
 
3.6%
22:001
 
3.6%

Most occurring characters

ValueCountFrequency (%)
075
53.6%
:28
 
20.0%
223
 
16.4%
19
 
6.4%
63
 
2.1%
91
 
0.7%
81
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number112
80.0%
Other Punctuation28
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
075
67.0%
223
 
20.5%
19
 
8.0%
63
 
2.7%
91
 
0.9%
81
 
0.9%
Other Punctuation
ValueCountFrequency (%)
:28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
075
53.6%
:28
 
20.0%
223
 
16.4%
19
 
6.4%
63
 
2.1%
91
 
0.7%
81
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
075
53.6%
:28
 
20.0%
223
 
16.4%
19
 
6.4%
63
 
2.1%
91
 
0.7%
81
 
0.7%

airstamp
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2020-12-18T12:00:00+00:00
102 
2020-12-18T06:30:00+00:00
21 
2020-12-18T11:00:00+00:00
15 
2020-12-18T04:00:00+00:00
11 
2020-12-18T17:00:00+00:00
 
5
Other values (9)
16 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters4250
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.9%

Sample

1st row2020-12-18T00:00:00+00:00
2nd row2020-12-18T00:00:00+00:00
3rd row2020-12-18T00:00:00+00:00
4th row2020-12-18T00:00:00+00:00
5th row2020-12-18T04:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-18T12:00:00+00:00102
60.0%
2020-12-18T06:30:00+00:0021
 
12.4%
2020-12-18T11:00:00+00:0015
 
8.8%
2020-12-18T04:00:00+00:0011
 
6.5%
2020-12-18T17:00:00+00:005
 
2.9%
2020-12-18T00:00:00+00:004
 
2.4%
2020-12-18T05:00:00+00:003
 
1.8%
2020-12-18T09:00:00+00:002
 
1.2%
2020-12-18T14:00:00+00:002
 
1.2%
2020-12-18T10:00:00+00:001
 
0.6%
Other values (4)4
 
2.4%

Length

2022-09-04T23:41:50.603706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-18t12:00:00+00:00102
60.0%
2020-12-18t06:30:00+00:0021
 
12.4%
2020-12-18t11:00:00+00:0015
 
8.8%
2020-12-18t04:00:00+00:0011
 
6.5%
2020-12-18t17:00:00+00:005
 
2.9%
2020-12-18t00:00:00+00:004
 
2.4%
2020-12-18t05:00:00+00:003
 
1.8%
2020-12-18t09:00:00+00:002
 
1.2%
2020-12-18t14:00:00+00:002
 
1.2%
2020-12-18t10:00:00+00:001
 
0.6%
Other values (4)4
 
2.4%

Most occurring characters

ValueCountFrequency (%)
01728
40.7%
2613
 
14.4%
:510
 
12.0%
1482
 
11.3%
-340
 
8.0%
T170
 
4.0%
+170
 
4.0%
8167
 
3.9%
322
 
0.5%
621
 
0.5%
Other values (4)27
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3060
72.0%
Other Punctuation510
 
12.0%
Dash Punctuation340
 
8.0%
Uppercase Letter170
 
4.0%
Math Symbol170
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01728
56.5%
2613
 
20.0%
1482
 
15.8%
8167
 
5.5%
322
 
0.7%
621
 
0.7%
413
 
0.4%
75
 
0.2%
95
 
0.2%
54
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:510
100.0%
Dash Punctuation
ValueCountFrequency (%)
-340
100.0%
Uppercase Letter
ValueCountFrequency (%)
T170
100.0%
Math Symbol
ValueCountFrequency (%)
+170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4080
96.0%
Latin170
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01728
42.4%
2613
 
15.0%
:510
 
12.5%
1482
 
11.8%
-340
 
8.3%
+170
 
4.2%
8167
 
4.1%
322
 
0.5%
621
 
0.5%
413
 
0.3%
Other values (3)14
 
0.3%
Latin
ValueCountFrequency (%)
T170
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01728
40.7%
2613
 
14.4%
:510
 
12.0%
1482
 
11.3%
-340
 
8.0%
T170
 
4.0%
+170
 
4.0%
8167
 
3.9%
322
 
0.5%
621
 
0.5%
Other values (4)27
 
0.6%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct53
Distinct (%)32.5%
Missing7
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean32.4601227
Minimum1
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:50.683706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q115
median32
Q345
95-th percentile59
Maximum120
Range119
Interquartile range (IQR)30

Descriptive statistics

Standard deviation18.94639788
Coefficient of variation (CV)0.5836822631
Kurtosis3.854514734
Mean32.4601227
Median Absolute Deviation (MAD)13
Skewness1.156951094
Sum5291
Variance358.9659926
MonotonicityNot monotonic
2022-09-04T23:41:50.779947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4526
 
15.3%
1512
 
7.1%
2511
 
6.5%
206
 
3.5%
85
 
2.9%
505
 
2.9%
94
 
2.4%
384
 
2.4%
604
 
2.4%
104
 
2.4%
Other values (43)82
48.2%
(Missing)7
 
4.1%
ValueCountFrequency (%)
11
 
0.6%
41
 
0.6%
52
 
1.2%
61
 
0.6%
72
 
1.2%
85
2.9%
94
2.4%
104
2.4%
112
 
1.2%
123
1.8%
ValueCountFrequency (%)
1202
1.2%
721
 
0.6%
631
 
0.6%
604
2.4%
592
1.2%
572
1.2%
552
1.2%
541
 
0.6%
532
1.2%
522
1.2%

image
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing170
Missing (%)100.0%
Memory size1.5 KiB

summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct66
Distinct (%)100.0%
Missing104
Missing (%)61.2%
Memory size1.5 KiB
<p>#InfluencersTheSeries has a SPECIAL LIVE EPISODE on December 18, 2020, 9PM. This will be a LIVE EPISODE and VIEWERS WILL BE PART OF IT FOR THE VERY FIRST TIME.</p>
 
1
<p>A military leaflet promises a route to safety, but the group is reluctant to trust it. Hyun Soo is exposed to a new perspective on his condition.</p>
 
1
<p>A special film : love story of Ji Han and Asin awaits us</p>
 
1
<p>Following the German declaration of war on America on the 11th of December 1941, Britain gained an invaluable ally. Securing a joint military command between the new partnership will be central to its success, the question is, how can this be achieved?</p>
 
1
<p>Mickey and his friends' plans for a barbeque get sidetracked after a quick trip to the supermarket turns into an odyssey.</p>
 
1
Other values (61)
61 

Length

Max length665
Median length251.5
Mean length251.1818182
Min length61

Characters and Unicode

Total characters16578
Distinct characters80
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)100.0%

Sample

1st row<p>Kim Runes gets a crack when Randi hires someone to fix the bathroom. Matheo steals from the medicine cabinet, and a riddle turns Christer's worldview upside down.</p>
2nd row<p>Trapped in horrible marriages, Veera, Jayati and Kavita, decide to get rid of their abusive husbands during a weekend getaway. While they manage to execute their seemingly perfect plan, things start getting worrisome when the police begin investigating the case and get their hands on some clues that could put the women in a spot. Also, Veera realise she and her daughter may be in grave danger.</p>
3rd row<p>The three widows try to get their lives back on track but face various unexpected situations that keep them on their toes. Unbeknownst to them, a different, sinister plot is unfolding at the lakehouse, and Rameez seems to be aware of it.</p>
4th row<p>Inspector Pankaj is certain he is on to something when he tries to trace the explosives used in the blast. Jatin tries to make sense of the attempt on his life, connecting it to his closely guarded secrets. The mysterious plot around MediPharma thickens even as the women, completely unaware of it, try to make sense of the turns their lives are taking.</p>
5th row<p>Veera has a new problem at hand which Jayati and Kavita must aid her with but, Kavita may be more of a hindrance than a help. Pankaj and Rinku's suspicion on the three women grows, and an unlikely witness may have given them an important clue. Inaaya Thakur, the boss of MediPharma, now begins to get involved as Rameez's death may complicate matters for her and finds an unexpected ally. Barry, Pankaj's boss, asks him to stop working on the case.</p>

Common Values

ValueCountFrequency (%)
<p>#InfluencersTheSeries has a SPECIAL LIVE EPISODE on December 18, 2020, 9PM. This will be a LIVE EPISODE and VIEWERS WILL BE PART OF IT FOR THE VERY FIRST TIME.</p>1
 
0.6%
<p>A military leaflet promises a route to safety, but the group is reluctant to trust it. Hyun Soo is exposed to a new perspective on his condition.</p>1
 
0.6%
<p>A special film : love story of Ji Han and Asin awaits us</p>1
 
0.6%
<p>Following the German declaration of war on America on the 11th of December 1941, Britain gained an invaluable ally. Securing a joint military command between the new partnership will be central to its success, the question is, how can this be achieved?</p>1
 
0.6%
<p>Mickey and his friends' plans for a barbeque get sidetracked after a quick trip to the supermarket turns into an odyssey.</p>1
 
0.6%
<p>Donald and Daisy's little lie becomes a big problem when they try to get out of a group date with Mickey and Minnie.</p>1
 
0.6%
<p>Amelia goes for a date to a chicken shop with Digga D.</p>1
 
0.6%
<p>So this time I passed through a very familiar shop I've seen over the years, but sometimes my curiosity ought not to be explored publicly. Mediocre epitomises its calibre. Obviously I forgot to slap burger sauce on the burger again, but that cheat code wouldn't have made a real difference, it was clearly unseasoned. Next episode should be a young Christmas spesh, so stay tuned.</p>1
 
0.6%
<p>Lost in the maze-like tunnels leading to the Piper's lair, Olivia and Wallace continue their search for Marcus and the others. But they're not alone in the dark...the Piper's children are hunting them too. Can the sound of Olivia's voice lure out the good that is left?</p>1
 
0.6%
<p>The school year begins at the prestigious School of American Ballet (SAB) in New York City. From Harlem to Chinatown, students as young as 6 years old audition for a coveted spot in SAB's Children's Division. Meanwhile, advanced students relocate from around the world to New York City where they will live and train<br />to be professional dancers under a seasoned and demanding faculty. As the students and teachers settle into their routines for the fall semester, preliminary casting for George Balanchine's The Nutcracker begins. Some flashing lights sequences or patterns may affect photosensitive viewers.</p>1
 
0.6%
Other values (56)56
32.9%
(Missing)104
61.2%

Length

2022-09-04T23:41:50.883947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the151
 
5.5%
to109
 
4.0%
a97
 
3.5%
and76
 
2.8%
of61
 
2.2%
in35
 
1.3%
their33
 
1.2%
her32
 
1.2%
for30
 
1.1%
is29
 
1.1%
Other values (1125)2099
76.3%

Most occurring characters

ValueCountFrequency (%)
2651
16.0%
e1539
 
9.3%
t1115
 
6.7%
a1095
 
6.6%
n960
 
5.8%
i924
 
5.6%
o865
 
5.2%
s860
 
5.2%
r817
 
4.9%
h651
 
3.9%
Other values (70)5101
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12466
75.2%
Space Separator2690
 
16.2%
Uppercase Letter541
 
3.3%
Other Punctuation464
 
2.8%
Math Symbol370
 
2.2%
Decimal Number25
 
0.2%
Dash Punctuation19
 
0.1%
Initial Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%
Open Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1539
12.3%
t1115
 
8.9%
a1095
 
8.8%
n960
 
7.7%
i924
 
7.4%
o865
 
6.9%
s860
 
6.9%
r817
 
6.6%
h651
 
5.2%
l455
 
3.6%
Other values (19)3185
25.5%
Uppercase Letter
ValueCountFrequency (%)
S45
 
8.3%
I36
 
6.7%
C35
 
6.5%
J35
 
6.5%
A34
 
6.3%
T32
 
5.9%
R32
 
5.9%
F26
 
4.8%
P24
 
4.4%
K23
 
4.3%
Other values (14)219
40.5%
Other Punctuation
ValueCountFrequency (%)
.159
34.3%
,123
26.5%
/104
22.4%
'50
 
10.8%
"14
 
3.0%
?7
 
1.5%
!3
 
0.6%
:2
 
0.4%
;1
 
0.2%
#1
 
0.2%
Decimal Number
ValueCountFrequency (%)
19
36.0%
25
20.0%
94
16.0%
03
 
12.0%
41
 
4.0%
31
 
4.0%
61
 
4.0%
81
 
4.0%
Space Separator
ValueCountFrequency (%)
2651
98.6%
 39
 
1.4%
Math Symbol
ValueCountFrequency (%)
<185
50.0%
>185
50.0%
Dash Punctuation
ValueCountFrequency (%)
-17
89.5%
2
 
10.5%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13007
78.5%
Common3571
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1539
11.8%
t1115
 
8.6%
a1095
 
8.4%
n960
 
7.4%
i924
 
7.1%
o865
 
6.7%
s860
 
6.6%
r817
 
6.3%
h651
 
5.0%
l455
 
3.5%
Other values (43)3726
28.6%
Common
ValueCountFrequency (%)
2651
74.2%
<185
 
5.2%
>185
 
5.2%
.159
 
4.5%
,123
 
3.4%
/104
 
2.9%
'50
 
1.4%
 39
 
1.1%
-17
 
0.5%
"14
 
0.4%
Other values (17)44
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII16523
99.7%
None52
 
0.3%
Punctuation3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2651
16.0%
e1539
 
9.3%
t1115
 
6.7%
a1095
 
6.6%
n960
 
5.8%
i924
 
5.6%
o865
 
5.2%
s860
 
5.2%
r817
 
4.9%
h651
 
3.9%
Other values (64)5046
30.5%
None
ValueCountFrequency (%)
 39
75.0%
í6
 
11.5%
ó6
 
11.5%
é1
 
1.9%
Punctuation
ValueCountFrequency (%)
2
66.7%
1
33.3%

rating.average
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct11
Distinct (%)37.9%
Missing141
Missing (%)82.9%
Infinite0
Infinite (%)0.0%
Mean8.486206897
Minimum6.1
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:50.965192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile7
Q17.7
median8.3
Q39.3
95-th percentile9.7
Maximum9.7
Range3.6
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.00631014
Coefficient of variation (CV)0.1185818532
Kurtosis-0.8138059866
Mean8.486206897
Median Absolute Deviation (MAD)1
Skewness-0.4625772808
Sum246.1
Variance1.012660099
MonotonicityNot monotonic
2022-09-04T23:41:51.033192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9.36
 
3.5%
7.74
 
2.4%
7.83
 
1.8%
8.33
 
1.8%
9.73
 
1.8%
9.53
 
1.8%
9.22
 
1.2%
72
 
1.2%
7.61
 
0.6%
7.51
 
0.6%
(Missing)141
82.9%
ValueCountFrequency (%)
6.11
 
0.6%
72
 
1.2%
7.51
 
0.6%
7.61
 
0.6%
7.74
2.4%
7.83
1.8%
8.33
1.8%
9.22
 
1.2%
9.36
3.5%
9.53
1.8%
ValueCountFrequency (%)
9.73
1.8%
9.53
1.8%
9.36
3.5%
9.22
 
1.2%
8.33
1.8%
7.83
1.8%
7.74
2.4%
7.61
 
0.6%
7.51
 
0.6%
72
 
1.2%

_links.self.href
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
https://api.tvmaze.com/episodes/1968114
 
1
https://api.tvmaze.com/episodes/1982920
 
1
https://api.tvmaze.com/episodes/1992735
 
1
https://api.tvmaze.com/episodes/1967697
 
1
https://api.tvmaze.com/episodes/1988120
 
1
Other values (165)
165 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters6630
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1968114
2nd rowhttps://api.tvmaze.com/episodes/1961005
3rd rowhttps://api.tvmaze.com/episodes/1989253
4th rowhttps://api.tvmaze.com/episodes/1988013
5th rowhttps://api.tvmaze.com/episodes/2030153

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19681141
 
0.6%
https://api.tvmaze.com/episodes/19829201
 
0.6%
https://api.tvmaze.com/episodes/19927351
 
0.6%
https://api.tvmaze.com/episodes/19676971
 
0.6%
https://api.tvmaze.com/episodes/19881201
 
0.6%
https://api.tvmaze.com/episodes/19881211
 
0.6%
https://api.tvmaze.com/episodes/19881221
 
0.6%
https://api.tvmaze.com/episodes/19881231
 
0.6%
https://api.tvmaze.com/episodes/19881241
 
0.6%
https://api.tvmaze.com/episodes/19993161
 
0.6%
Other values (160)160
94.1%

Length

2022-09-04T23:41:51.111299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19681141
 
0.6%
https://api.tvmaze.com/episodes/19985831
 
0.6%
https://api.tvmaze.com/episodes/19902381
 
0.6%
https://api.tvmaze.com/episodes/19902371
 
0.6%
https://api.tvmaze.com/episodes/19892531
 
0.6%
https://api.tvmaze.com/episodes/19880131
 
0.6%
https://api.tvmaze.com/episodes/20301531
 
0.6%
https://api.tvmaze.com/episodes/19725691
 
0.6%
https://api.tvmaze.com/episodes/19725701
 
0.6%
https://api.tvmaze.com/episodes/19104481
 
0.6%
Other values (160)160
94.1%

Most occurring characters

ValueCountFrequency (%)
/680
 
10.3%
p510
 
7.7%
s510
 
7.7%
e510
 
7.7%
t510
 
7.7%
o340
 
5.1%
a340
 
5.1%
i340
 
5.1%
.340
 
5.1%
m340
 
5.1%
Other values (16)2210
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4250
64.1%
Other Punctuation1190
 
17.9%
Decimal Number1190
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p510
12.0%
s510
12.0%
e510
12.0%
t510
12.0%
o340
8.0%
a340
8.0%
i340
8.0%
m340
8.0%
h170
 
4.0%
d170
 
4.0%
Other values (3)510
12.0%
Decimal Number
ValueCountFrequency (%)
1223
18.7%
9214
18.0%
2135
11.3%
8122
10.3%
0116
9.7%
579
 
6.6%
378
 
6.6%
476
 
6.4%
776
 
6.4%
671
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/680
57.1%
.340
28.6%
:170
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4250
64.1%
Common2380
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/680
28.6%
.340
14.3%
1223
 
9.4%
9214
 
9.0%
:170
 
7.1%
2135
 
5.7%
8122
 
5.1%
0116
 
4.9%
579
 
3.3%
378
 
3.3%
Other values (3)223
 
9.4%
Latin
ValueCountFrequency (%)
p510
12.0%
s510
12.0%
e510
12.0%
t510
12.0%
o340
8.0%
a340
8.0%
i340
8.0%
m340
8.0%
h170
 
4.0%
d170
 
4.0%
Other values (3)510
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/680
 
10.3%
p510
 
7.7%
s510
 
7.7%
e510
 
7.7%
t510
 
7.7%
o340
 
5.1%
a340
 
5.1%
i340
 
5.1%
.340
 
5.1%
m340
 
5.1%
Other values (16)2210
33.3%

_embedded.show.id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48055.17647
Minimum7847
Maximum62418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:51.191299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7847
5-th percentile24453
Q144117
median51940.5
Q352593
95-th percentile57213
Maximum62418
Range54571
Interquartile range (IQR)8476

Descriptive statistics

Standard deviation9499.588159
Coefficient of variation (CV)0.1976808506
Kurtosis5.494625568
Mean48055.17647
Median Absolute Deviation (MAD)2821.5
Skewness-2.206340582
Sum8169380
Variance90242175.2
MonotonicityNot monotonic
2022-09-04T23:41:51.271498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5259312
 
7.1%
4411710
 
5.9%
4657010
 
5.9%
525828
 
4.7%
538117
 
4.1%
524516
 
3.5%
414896
 
3.5%
518366
 
3.5%
410675
 
2.9%
570294
 
2.4%
Other values (80)96
56.5%
ValueCountFrequency (%)
78471
0.6%
115022
1.2%
152502
1.2%
191111
0.6%
207341
0.6%
210351
0.6%
225361
0.6%
267961
0.6%
306061
0.6%
354201
0.6%
ValueCountFrequency (%)
624181
 
0.6%
611401
 
0.6%
583672
1.2%
574911
 
0.6%
573501
 
0.6%
572134
2.4%
570294
2.4%
568481
 
0.6%
564331
 
0.6%
561391
 
0.6%

_embedded.show.url
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct90
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
https://www.tvmaze.com/shows/52593/black-widows
 
12
https://www.tvmaze.com/shows/44117/red
 
10
https://www.tvmaze.com/shows/46570/sweet-home
 
10
https://www.tvmaze.com/shows/52582/taqdeer
 
8
https://www.tvmaze.com/shows/53811/bildmachtdeutschland
 
7
Other values (85)
123 

Length

Max length70
Median length61
Mean length48.48235294
Min length38

Characters and Unicode

Total characters8242
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)38.8%

Sample

1st rowhttps://www.tvmaze.com/shows/7847/po-sezonu-videodajdzest-seasonvar
2nd rowhttps://www.tvmaze.com/shows/48402/cuma
3rd rowhttps://www.tvmaze.com/shows/48403/to-so-sketci
4th rowhttps://www.tvmaze.com/shows/52520/muzskaa-tema
5th rowhttps://www.tvmaze.com/shows/20734/fox-spirit-matchmaker

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52593/black-widows12
 
7.1%
https://www.tvmaze.com/shows/44117/red10
 
5.9%
https://www.tvmaze.com/shows/46570/sweet-home10
 
5.9%
https://www.tvmaze.com/shows/52582/taqdeer8
 
4.7%
https://www.tvmaze.com/shows/53811/bildmachtdeutschland7
 
4.1%
https://www.tvmaze.com/shows/52451/the-burning-river6
 
3.5%
https://www.tvmaze.com/shows/41489/hjem-til-jul6
 
3.5%
https://www.tvmaze.com/shows/51836/on-pointe6
 
3.5%
https://www.tvmaze.com/shows/41067/el-cid5
 
2.9%
https://www.tvmaze.com/shows/57029/bablo4
 
2.4%
Other values (80)96
56.5%

Length

2022-09-04T23:41:51.356503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52593/black-widows12
 
7.1%
https://www.tvmaze.com/shows/44117/red10
 
5.9%
https://www.tvmaze.com/shows/46570/sweet-home10
 
5.9%
https://www.tvmaze.com/shows/52582/taqdeer8
 
4.7%
https://www.tvmaze.com/shows/53811/bildmachtdeutschland7
 
4.1%
https://www.tvmaze.com/shows/52451/the-burning-river6
 
3.5%
https://www.tvmaze.com/shows/41489/hjem-til-jul6
 
3.5%
https://www.tvmaze.com/shows/51836/on-pointe6
 
3.5%
https://www.tvmaze.com/shows/41067/el-cid5
 
2.9%
https://www.tvmaze.com/shows/57029/bablo4
 
2.4%
Other values (80)96
56.5%

Most occurring characters

ValueCountFrequency (%)
/850
 
10.3%
w734
 
8.9%
t647
 
7.9%
s630
 
7.6%
o480
 
5.8%
h432
 
5.2%
e432
 
5.2%
m423
 
5.1%
.340
 
4.1%
a328
 
4.0%
Other values (30)2946
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5807
70.5%
Other Punctuation1360
 
16.5%
Decimal Number852
 
10.3%
Dash Punctuation223
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w734
12.6%
t647
11.1%
s630
10.8%
o480
 
8.3%
h432
 
7.4%
e432
 
7.4%
m423
 
7.3%
a328
 
5.6%
c237
 
4.1%
p203
 
3.5%
Other values (16)1261
21.7%
Decimal Number
ValueCountFrequency (%)
5172
20.2%
1117
13.7%
4104
12.2%
2100
11.7%
368
 
8.0%
766
 
7.7%
060
 
7.0%
657
 
6.7%
857
 
6.7%
951
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/850
62.5%
.340
 
25.0%
:170
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-223
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5807
70.5%
Common2435
29.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w734
12.6%
t647
11.1%
s630
10.8%
o480
 
8.3%
h432
 
7.4%
e432
 
7.4%
m423
 
7.3%
a328
 
5.6%
c237
 
4.1%
p203
 
3.5%
Other values (16)1261
21.7%
Common
ValueCountFrequency (%)
/850
34.9%
.340
 
14.0%
-223
 
9.2%
5172
 
7.1%
:170
 
7.0%
1117
 
4.8%
4104
 
4.3%
2100
 
4.1%
368
 
2.8%
766
 
2.7%
Other values (4)225
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/850
 
10.3%
w734
 
8.9%
t647
 
7.9%
s630
 
7.6%
o480
 
5.8%
h432
 
5.2%
e432
 
5.2%
m423
 
5.1%
.340
 
4.1%
a328
 
4.0%
Other values (30)2946
35.7%

_embedded.show.name
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct90
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Black Widows
 
12
RED
 
10
Sweet Home
 
10
Taqdeer
 
8
BILD.Macht.Deutschland?
 
7
Other values (85)
123 

Length

Max length35
Median length24
Mean length13.71764706
Min length3

Characters and Unicode

Total characters2332
Distinct characters103
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)38.8%

Sample

1st rowПо сезону. Видеодайджест Seasonvar
2nd rowЧума!
3rd rowТо шо скетчи
4th rowМужская тема
5th rowFox Spirit Matchmaker

Common Values

ValueCountFrequency (%)
Black Widows12
 
7.1%
RED10
 
5.9%
Sweet Home10
 
5.9%
Taqdeer8
 
4.7%
BILD.Macht.Deutschland?7
 
4.1%
The Burning River6
 
3.5%
Hjem til jul6
 
3.5%
On Pointe6
 
3.5%
El Cid5
 
2.9%
Bablo4
 
2.4%
Other values (80)96
56.5%

Length

2022-09-04T23:41:51.553385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the24
 
6.2%
black12
 
3.1%
home12
 
3.1%
widows12
 
3.1%
red11
 
2.8%
sweet10
 
2.6%
taqdeer8
 
2.1%
bild.macht.deutschland7
 
1.8%
til7
 
1.8%
jul6
 
1.5%
Other values (196)281
72.1%

Most occurring characters

ValueCountFrequency (%)
e236
 
10.1%
220
 
9.4%
a140
 
6.0%
o116
 
5.0%
i113
 
4.8%
r101
 
4.3%
n100
 
4.3%
t97
 
4.2%
l92
 
3.9%
s83
 
3.6%
Other values (93)1034
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1655
71.0%
Uppercase Letter413
 
17.7%
Space Separator220
 
9.4%
Other Punctuation38
 
1.6%
Dash Punctuation3
 
0.1%
Decimal Number3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e236
14.3%
a140
 
8.5%
o116
 
7.0%
i113
 
6.8%
r101
 
6.1%
n100
 
6.0%
t97
 
5.9%
l92
 
5.6%
s83
 
5.0%
d66
 
4.0%
Other values (44)511
30.9%
Uppercase Letter
ValueCountFrequency (%)
B36
 
8.7%
D36
 
8.7%
T34
 
8.2%
M33
 
8.0%
S30
 
7.3%
R27
 
6.5%
H26
 
6.3%
W25
 
6.1%
L21
 
5.1%
C19
 
4.6%
Other values (28)126
30.5%
Other Punctuation
ValueCountFrequency (%)
.17
44.7%
?9
23.7%
:6
 
15.8%
!3
 
7.9%
'2
 
5.3%
,1
 
2.6%
Decimal Number
ValueCountFrequency (%)
21
33.3%
01
33.3%
51
33.3%
Space Separator
ValueCountFrequency (%)
220
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1982
85.0%
Common264
 
11.3%
Cyrillic86
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e236
 
11.9%
a140
 
7.1%
o116
 
5.9%
i113
 
5.7%
r101
 
5.1%
n100
 
5.0%
t97
 
4.9%
l92
 
4.6%
s83
 
4.2%
d66
 
3.3%
Other values (47)838
42.3%
Cyrillic
ValueCountFrequency (%)
о9
 
10.5%
е7
 
8.1%
а7
 
8.1%
и6
 
7.0%
с6
 
7.0%
д5
 
5.8%
к4
 
4.7%
т4
 
4.7%
м3
 
3.5%
р3
 
3.5%
Other values (25)32
37.2%
Common
ValueCountFrequency (%)
220
83.3%
.17
 
6.4%
?9
 
3.4%
:6
 
2.3%
-3
 
1.1%
!3
 
1.1%
'2
 
0.8%
21
 
0.4%
01
 
0.4%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2238
96.0%
Cyrillic86
 
3.7%
None8
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e236
 
10.5%
220
 
9.8%
a140
 
6.3%
o116
 
5.2%
i113
 
5.0%
r101
 
4.5%
n100
 
4.5%
t97
 
4.3%
l92
 
4.1%
s83
 
3.7%
Other values (53)940
42.0%
Cyrillic
ValueCountFrequency (%)
о9
 
10.5%
е7
 
8.1%
а7
 
8.1%
и6
 
7.0%
с6
 
7.0%
д5
 
5.8%
к4
 
4.7%
т4
 
4.7%
м3
 
3.5%
р3
 
3.5%
Other values (25)32
37.2%
None
ValueCountFrequency (%)
ø3
37.5%
å2
25.0%
é1
 
12.5%
ä1
 
12.5%
á1
 
12.5%

_embedded.show.type
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Scripted
112 
Documentary
25 
Animation
 
11
Talk Show
 
8
Reality
 
7
Other values (3)
 
7

Length

Max length11
Median length8
Mean length8.429411765
Min length4

Characters and Unicode

Total characters1433
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowTalk Show
2nd rowScripted
3rd rowVariety
4th rowTalk Show
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted112
65.9%
Documentary25
 
14.7%
Animation11
 
6.5%
Talk Show8
 
4.7%
Reality7
 
4.1%
Variety4
 
2.4%
News2
 
1.2%
Sports1
 
0.6%

Length

2022-09-04T23:41:51.631512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:51.714658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
scripted112
62.9%
documentary25
 
14.0%
animation11
 
6.2%
talk8
 
4.5%
show8
 
4.5%
reality7
 
3.9%
variety4
 
2.2%
news2
 
1.1%
sports1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
t160
11.2%
e150
10.5%
i145
10.1%
r142
9.9%
c137
9.6%
S121
8.4%
p113
7.9%
d112
7.8%
a55
 
3.8%
n47
 
3.3%
Other values (16)251
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1247
87.0%
Uppercase Letter178
 
12.4%
Space Separator8
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t160
12.8%
e150
12.0%
i145
11.6%
r142
11.4%
c137
11.0%
p113
9.1%
d112
9.0%
a55
 
4.4%
n47
 
3.8%
o45
 
3.6%
Other values (8)141
11.3%
Uppercase Letter
ValueCountFrequency (%)
S121
68.0%
D25
 
14.0%
A11
 
6.2%
T8
 
4.5%
R7
 
3.9%
V4
 
2.2%
N2
 
1.1%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1425
99.4%
Common8
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t160
11.2%
e150
10.5%
i145
10.2%
r142
10.0%
c137
9.6%
S121
8.5%
p113
7.9%
d112
7.9%
a55
 
3.9%
n47
 
3.3%
Other values (15)243
17.1%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1433
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t160
11.2%
e150
10.5%
i145
10.1%
r142
9.9%
c137
9.6%
S121
8.4%
p113
7.9%
d112
7.8%
a55
 
3.8%
n47
 
3.3%
Other values (16)251
17.5%

_embedded.show.language
Categorical

HIGH CORRELATION
MISSING

Distinct18
Distinct (%)10.7%
Missing2
Missing (%)1.2%
Memory size1.5 KiB
English
33 
Chinese
24 
Norwegian
20 
Korean
18 
Portuguese
14 
Other values (13)
59 

Length

Max length10
Median length7
Mean length7.077380952
Min length4

Characters and Unicode

Total characters1189
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.8%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowRussian
5th rowChinese

Common Values

ValueCountFrequency (%)
English33
19.4%
Chinese24
14.1%
Norwegian20
11.8%
Korean18
10.6%
Portuguese14
8.2%
Hindi13
 
7.6%
Bengali8
 
4.7%
Spanish8
 
4.7%
German8
 
4.7%
Russian7
 
4.1%
Other values (8)15
8.8%

Length

2022-09-04T23:41:51.803106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english33
19.6%
chinese24
14.3%
norwegian20
11.9%
korean18
10.7%
portuguese14
8.3%
hindi13
 
7.7%
bengali8
 
4.8%
spanish8
 
4.8%
german8
 
4.8%
russian7
 
4.2%
Other values (8)15
8.9%

Most occurring characters

ValueCountFrequency (%)
n142
11.9%
i136
11.4%
e133
11.2%
s97
 
8.2%
a82
 
6.9%
g79
 
6.6%
h72
 
6.1%
r61
 
5.1%
o55
 
4.6%
l48
 
4.0%
Other values (23)284
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1021
85.9%
Uppercase Letter168
 
14.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n142
13.9%
i136
13.3%
e133
13.0%
s97
9.5%
a82
8.0%
g79
7.7%
h72
7.1%
r61
6.0%
o55
 
5.4%
l48
 
4.7%
Other values (9)116
11.4%
Uppercase Letter
ValueCountFrequency (%)
E33
19.6%
C24
14.3%
N20
11.9%
K18
10.7%
P14
8.3%
H13
 
7.7%
S10
 
6.0%
T9
 
5.4%
B8
 
4.8%
G8
 
4.8%
Other values (4)11
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1189
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n142
11.9%
i136
11.4%
e133
11.2%
s97
 
8.2%
a82
 
6.9%
g79
 
6.6%
h72
 
6.1%
r61
 
5.1%
o55
 
4.6%
l48
 
4.0%
Other values (23)284
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n142
11.9%
i136
11.4%
e133
11.2%
s97
 
8.2%
a82
 
6.9%
g79
 
6.6%
h72
 
6.1%
r61
 
5.1%
o55
 
4.6%
l48
 
4.0%
Other values (23)284
23.9%

_embedded.show.genres
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.5 KiB

_embedded.show.status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Ended
79 
Running
70 
To Be Determined
21 

Length

Max length16
Median length7
Mean length7.182352941
Min length5

Characters and Unicode

Total characters1221
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowEnded
3rd rowEnded
4th rowEnded
5th rowRunning

Common Values

ValueCountFrequency (%)
Ended79
46.5%
Running70
41.2%
To Be Determined21
 
12.4%

Length

2022-09-04T23:41:51.879977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:51.959377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ended79
37.3%
running70
33.0%
to21
 
9.9%
be21
 
9.9%
determined21
 
9.9%

Most occurring characters

ValueCountFrequency (%)
n310
25.4%
d179
14.7%
e163
13.3%
i91
 
7.5%
E79
 
6.5%
R70
 
5.7%
u70
 
5.7%
g70
 
5.7%
42
 
3.4%
T21
 
1.7%
Other values (6)126
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter967
79.2%
Uppercase Letter212
 
17.4%
Space Separator42
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n310
32.1%
d179
18.5%
e163
16.9%
i91
 
9.4%
u70
 
7.2%
g70
 
7.2%
o21
 
2.2%
t21
 
2.2%
r21
 
2.2%
m21
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
E79
37.3%
R70
33.0%
T21
 
9.9%
B21
 
9.9%
D21
 
9.9%
Space Separator
ValueCountFrequency (%)
42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1179
96.6%
Common42
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n310
26.3%
d179
15.2%
e163
13.8%
i91
 
7.7%
E79
 
6.7%
R70
 
5.9%
u70
 
5.9%
g70
 
5.9%
T21
 
1.8%
o21
 
1.8%
Other values (5)105
 
8.9%
Common
ValueCountFrequency (%)
42
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n310
25.4%
d179
14.7%
e163
13.3%
i91
 
7.5%
E79
 
6.5%
R70
 
5.7%
u70
 
5.7%
g70
 
5.7%
42
 
3.4%
T21
 
1.7%
Other values (6)126
10.3%

_embedded.show.runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)22.7%
Missing60
Missing (%)35.3%
Infinite0
Infinite (%)0.0%
Mean30.53636364
Minimum1
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:52.031752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q115
median30
Q345
95-th percentile50
Maximum120
Range119
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.15301843
Coefficient of variation (CV)0.6272200142
Kurtosis6.84152606
Mean30.53636364
Median Absolute Deviation (MAD)15
Skewness1.784973584
Sum3359
Variance366.8381151
MonotonicityNot monotonic
2022-09-04T23:41:52.110866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4525
14.7%
1515
 
8.8%
3512
 
7.1%
2511
 
6.5%
108
 
4.7%
306
 
3.5%
205
 
2.9%
503
 
1.8%
603
 
1.8%
52
 
1.2%
Other values (15)20
 
11.8%
(Missing)60
35.3%
ValueCountFrequency (%)
11
 
0.6%
52
 
1.2%
72
 
1.2%
82
 
1.2%
91
 
0.6%
108
4.7%
112
 
1.2%
141
 
0.6%
1515
8.8%
161
 
0.6%
ValueCountFrequency (%)
1202
 
1.2%
603
 
1.8%
503
 
1.8%
481
 
0.6%
4525
14.7%
422
 
1.2%
401
 
0.6%
381
 
0.6%
3512
7.1%
306
 
3.5%

_embedded.show.averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)23.5%
Missing4
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean32.04216867
Minimum1
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:52.191967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q114.25
median30
Q345
95-th percentile59
Maximum120
Range119
Interquartile range (IQR)30.75

Descriptive statistics

Standard deviation19.06618533
Coefficient of variation (CV)0.5950341728
Kurtosis3.735767321
Mean32.04216867
Median Absolute Deviation (MAD)15
Skewness1.142680682
Sum5319
Variance363.5194231
MonotonicityNot monotonic
2022-09-04T23:41:52.281523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4531
18.2%
2514
 
8.2%
3512
 
7.1%
1412
 
7.1%
5210
 
5.9%
308
 
4.7%
87
 
4.1%
106
 
3.5%
116
 
3.5%
595
 
2.9%
Other values (29)55
32.4%
ValueCountFrequency (%)
11
 
0.6%
53
 
1.8%
61
 
0.6%
72
 
1.2%
87
4.1%
91
 
0.6%
106
3.5%
116
3.5%
123
 
1.8%
1412
7.1%
ValueCountFrequency (%)
1202
 
1.2%
761
 
0.6%
603
 
1.8%
595
 
2.9%
542
 
1.2%
5210
 
5.9%
503
 
1.8%
481
 
0.6%
461
 
0.6%
4531
18.2%

_embedded.show.premiered
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct69
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2020-12-18
53 
2014-09-27
10 
2020-12-04
 
8
2020-12-11
 
8
2019-12-05
 
6
Other values (64)
85 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)28.8%

Sample

1st row2015-02-13
2nd row2020-05-29
3rd row2020-05-25
4th row2020-12-17
5th row2015-06-26

Common Values

ValueCountFrequency (%)
2020-12-1853
31.2%
2014-09-2710
 
5.9%
2020-12-048
 
4.7%
2020-12-118
 
4.7%
2019-12-056
 
3.5%
2020-12-105
 
2.9%
2020-04-144
 
2.4%
2020-11-273
 
1.8%
2020-11-182
 
1.2%
2020-12-022
 
1.2%
Other values (59)69
40.6%

Length

2022-09-04T23:41:52.366524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-1853
31.2%
2014-09-2710
 
5.9%
2020-12-048
 
4.7%
2020-12-118
 
4.7%
2019-12-056
 
3.5%
2020-12-105
 
2.9%
2020-04-144
 
2.4%
2020-11-273
 
1.8%
2012-01-282
 
1.2%
2020-11-192
 
1.2%
Other values (59)69
40.6%

Most occurring characters

ValueCountFrequency (%)
2432
25.4%
0384
22.6%
-340
20.0%
1313
18.4%
869
 
4.1%
944
 
2.6%
439
 
2.3%
728
 
1.6%
521
 
1.2%
617
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1360
80.0%
Dash Punctuation340
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2432
31.8%
0384
28.2%
1313
23.0%
869
 
5.1%
944
 
3.2%
439
 
2.9%
728
 
2.1%
521
 
1.5%
617
 
1.2%
313
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
-340
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2432
25.4%
0384
22.6%
-340
20.0%
1313
18.4%
869
 
4.1%
944
 
2.6%
439
 
2.3%
728
 
1.6%
521
 
1.2%
617
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2432
25.4%
0384
22.6%
-340
20.0%
1313
18.4%
869
 
4.1%
944
 
2.6%
439
 
2.3%
728
 
1.6%
521
 
1.2%
617
 
1.0%

_embedded.show.ended
Categorical

HIGH CORRELATION
MISSING

Distinct21
Distinct (%)26.6%
Missing91
Missing (%)53.5%
Memory size1.5 KiB
2020-12-18
42 
2021-01-01
2020-12-25
2021-01-22
 
2
2021-01-02
 
2
Other values (16)
21 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters790
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)13.9%

Sample

1st row2020-12-18
2nd row2021-03-11
3rd row2020-12-25
4th row2021-01-04
5th row2021-01-04

Common Values

ValueCountFrequency (%)
2020-12-1842
24.7%
2021-01-017
 
4.1%
2020-12-255
 
2.9%
2021-01-222
 
1.2%
2021-01-022
 
1.2%
2020-12-222
 
1.2%
2021-01-092
 
1.2%
2021-01-142
 
1.2%
2021-01-292
 
1.2%
2021-01-042
 
1.2%
Other values (11)11
 
6.5%
(Missing)91
53.5%

Length

2022-09-04T23:41:52.440524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-1842
53.2%
2021-01-017
 
8.9%
2020-12-255
 
6.3%
2021-01-222
 
2.5%
2021-01-022
 
2.5%
2020-12-222
 
2.5%
2021-01-092
 
2.5%
2021-01-142
 
2.5%
2021-01-292
 
2.5%
2021-01-042
 
2.5%
Other values (11)11
 
13.9%

Most occurring characters

ValueCountFrequency (%)
2233
29.5%
0176
22.3%
1159
20.1%
-158
20.0%
844
 
5.6%
58
 
1.0%
45
 
0.6%
94
 
0.5%
32
 
0.3%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number632
80.0%
Dash Punctuation158
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2233
36.9%
0176
27.8%
1159
25.2%
844
 
7.0%
58
 
1.3%
45
 
0.8%
94
 
0.6%
32
 
0.3%
61
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
-158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common790
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2233
29.5%
0176
22.3%
1159
20.1%
-158
20.0%
844
 
5.6%
58
 
1.0%
45
 
0.6%
94
 
0.5%
32
 
0.3%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2233
29.5%
0176
22.3%
1159
20.1%
-158
20.0%
844
 
5.6%
58
 
1.0%
45
 
0.6%
94
 
0.5%
32
 
0.3%
61
 
0.1%

_embedded.show.officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct80
Distinct (%)55.9%
Missing27
Missing (%)15.9%
Memory size1.5 KiB
https://www.zee5.com/global/zee5originals/details/black-widows/0-6-3029
12 
https://www.netflix.com/title/81061734
 
10
https://www.hoichoi.tv/webseries/taqdeer
 
8
https://www.amazon.de/BILD-Macht-Deutschland-Staffel-1/dp/B08PS4JGRM
 
7
https://v.youku.com/v_show/id_XNDk1MzY2NzgwNA==.html?spm=a2h0c.8166622.PhoneSokuProgram_1.dtitle&s=aaed627feea749d7a99d
 
6
Other values (75)
100 

Length

Max length250
Median length78
Mean length54.88111888
Min length26

Characters and Unicode

Total characters7848
Distinct characters73
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)42.7%

Sample

1st rowhttp://seasonvar.ru/serial-11488-Po_sezonu_Videodajdzhest_Seasonvar.html
2nd rowhttps://www.ivi.ru/watch/chuma-2020
3rd rowhttps://premier.one/show/to-sho-sketchi
4th rowhttps://www.ivi.ru/watch/muzhskaya-tema
5th rowhttp://www.bilibili.com/bangumi/%E7%8B%90%E5%A6%96%E5%B0%8F%E7%BA%A2%E5%A8%98/

Common Values

ValueCountFrequency (%)
https://www.zee5.com/global/zee5originals/details/black-widows/0-6-302912
 
7.1%
https://www.netflix.com/title/8106173410
 
5.9%
https://www.hoichoi.tv/webseries/taqdeer8
 
4.7%
https://www.amazon.de/BILD-Macht-Deutschland-Staffel-1/dp/B08PS4JGRM7
 
4.1%
https://v.youku.com/v_show/id_XNDk1MzY2NzgwNA==.html?spm=a2h0c.8166622.PhoneSokuProgram_1.dtitle&s=aaed627feea749d7a99d6
 
3.5%
https://www.netflix.com/title/810835906
 
3.5%
https://www.amazon.com/dp/B08NSTBD1Q/5
 
2.9%
https://www.netflix.com/title/813412164
 
2.4%
https://tv.nrk.no/serie/bablo4
 
2.4%
https://www.disneyplus.com/en-gb/series/the-wonderful-world-of-mickey-mouse/6PVlUlZEyNjS2
 
1.2%
Other values (70)79
46.5%
(Missing)27
 
15.9%

Length

2022-09-04T23:41:52.529156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.zee5.com/global/zee5originals/details/black-widows/0-6-302912
 
8.4%
https://www.netflix.com/title/8106173410
 
7.0%
https://www.hoichoi.tv/webseries/taqdeer8
 
5.6%
https://www.amazon.de/bild-macht-deutschland-staffel-1/dp/b08ps4jgrm7
 
4.9%
https://v.youku.com/v_show/id_xndk1mzy2nzgwna==.html?spm=a2h0c.8166622.phonesokuprogram_1.dtitle&s=aaed627feea749d7a99d6
 
4.2%
https://www.netflix.com/title/810835906
 
4.2%
https://www.amazon.com/dp/b08nstbd1q5
 
3.5%
https://www.netflix.com/title/813412164
 
2.8%
https://tv.nrk.no/serie/bablo4
 
2.8%
https://play.tv2.no/programmer/underholdning/hver-gang-vi-moetes2
 
1.4%
Other values (70)79
55.2%

Most occurring characters

ValueCountFrequency (%)
/635
 
8.1%
t554
 
7.1%
e418
 
5.3%
w404
 
5.1%
s373
 
4.8%
o336
 
4.3%
.317
 
4.0%
i289
 
3.7%
a279
 
3.6%
h278
 
3.5%
Other values (63)3965
50.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5071
64.6%
Other Punctuation1177
 
15.0%
Decimal Number910
 
11.6%
Uppercase Letter450
 
5.7%
Dash Punctuation147
 
1.9%
Math Symbol50
 
0.6%
Connector Punctuation43
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t554
 
10.9%
e418
 
8.2%
w404
 
8.0%
s373
 
7.4%
o336
 
6.6%
i289
 
5.7%
a279
 
5.5%
h278
 
5.5%
l253
 
5.0%
p218
 
4.3%
Other values (16)1669
32.9%
Uppercase Letter
ValueCountFrequency (%)
S39
 
8.7%
D38
 
8.4%
B36
 
8.0%
N34
 
7.6%
P34
 
7.6%
M29
 
6.4%
A23
 
5.1%
L19
 
4.2%
E18
 
4.0%
R16
 
3.6%
Other values (16)164
36.4%
Decimal Number
ValueCountFrequency (%)
1146
16.0%
0126
13.8%
293
10.2%
690
9.9%
485
9.3%
883
9.1%
379
8.7%
574
8.1%
972
7.9%
762
6.8%
Other Punctuation
ValueCountFrequency (%)
/635
54.0%
.317
26.9%
:161
 
13.7%
%33
 
2.8%
?18
 
1.5%
&13
 
1.1%
Math Symbol
ValueCountFrequency (%)
=47
94.0%
+2
 
4.0%
~1
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
-147
100.0%
Connector Punctuation
ValueCountFrequency (%)
_43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5521
70.3%
Common2327
29.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t554
 
10.0%
e418
 
7.6%
w404
 
7.3%
s373
 
6.8%
o336
 
6.1%
i289
 
5.2%
a279
 
5.1%
h278
 
5.0%
l253
 
4.6%
p218
 
3.9%
Other values (42)2119
38.4%
Common
ValueCountFrequency (%)
/635
27.3%
.317
13.6%
:161
 
6.9%
-147
 
6.3%
1146
 
6.3%
0126
 
5.4%
293
 
4.0%
690
 
3.9%
485
 
3.7%
883
 
3.6%
Other values (11)444
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/635
 
8.1%
t554
 
7.1%
e418
 
5.3%
w404
 
5.1%
s373
 
4.8%
o336
 
4.3%
.317
 
4.0%
i289
 
3.7%
a279
 
3.6%
h278
 
3.5%
Other values (63)3965
50.5%

_embedded.show.schedule.time
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
145 
18:00
 
7
12:00
 
4
20:00
 
4
06:00
 
3
Other values (5)
 
7

Length

Max length5
Median length0
Mean length0.7352941176
Min length0

Characters and Unicode

Total characters125
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.8%

Sample

1st row
2nd row
3rd row
4th row12:00
5th row

Common Values

ValueCountFrequency (%)
145
85.3%
18:007
 
4.1%
12:004
 
2.4%
20:004
 
2.4%
06:003
 
1.8%
00:002
 
1.2%
21:002
 
1.2%
10:001
 
0.6%
19:001
 
0.6%
22:001
 
0.6%

Length

2022-09-04T23:41:52.613156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:52.701156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
18:007
28.0%
12:004
16.0%
20:004
16.0%
06:003
12.0%
00:002
 
8.0%
21:002
 
8.0%
10:001
 
4.0%
19:001
 
4.0%
22:001
 
4.0%

Most occurring characters

ValueCountFrequency (%)
062
49.6%
:25
20.0%
115
 
12.0%
212
 
9.6%
87
 
5.6%
63
 
2.4%
91
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100
80.0%
Other Punctuation25
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
062
62.0%
115
 
15.0%
212
 
12.0%
87
 
7.0%
63
 
3.0%
91
 
1.0%
Other Punctuation
ValueCountFrequency (%)
:25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common125
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
062
49.6%
:25
20.0%
115
 
12.0%
212
 
9.6%
87
 
5.6%
63
 
2.4%
91
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
062
49.6%
:25
20.0%
115
 
12.0%
212
 
9.6%
87
 
5.6%
63
 
2.4%
91
 
0.8%

_embedded.show.schedule.days
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.5 KiB

_embedded.show.rating.average
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct11
Distinct (%)35.5%
Missing139
Missing (%)81.8%
Infinite0
Infinite (%)0.0%
Mean6.841935484
Minimum4.3
Maximum8.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:52.777156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile4.7
Q16.9
median7.1
Q37.2
95-th percentile7.5
Maximum8.6
Range4.3
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.8800659799
Coefficient of variation (CV)0.1286282196
Kurtosis3.482498615
Mean6.841935484
Median Absolute Deviation (MAD)0.1
Skewness-1.639273686
Sum212.1
Variance0.774516129
MonotonicityNot monotonic
2022-09-04T23:41:52.840156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.212
 
7.1%
7.16
 
3.5%
6.95
 
2.9%
61
 
0.6%
6.41
 
0.6%
7.81
 
0.6%
8.61
 
0.6%
51
 
0.6%
6.11
 
0.6%
4.31
 
0.6%
(Missing)139
81.8%
ValueCountFrequency (%)
4.31
 
0.6%
4.41
 
0.6%
51
 
0.6%
61
 
0.6%
6.11
 
0.6%
6.41
 
0.6%
6.95
2.9%
7.16
3.5%
7.212
7.1%
7.81
 
0.6%
ValueCountFrequency (%)
8.61
 
0.6%
7.81
 
0.6%
7.212
7.1%
7.16
3.5%
6.95
2.9%
6.41
 
0.6%
6.11
 
0.6%
61
 
0.6%
51
 
0.6%
4.41
 
0.6%

_embedded.show.weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.48823529
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:52.921294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q117
median24
Q347
95-th percentile92
Maximum100
Range97
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.65919876
Coefficient of variation (CV)0.7729939944
Kurtosis0.04317659421
Mean34.48823529
Median Absolute Deviation (MAD)13
Skewness1.039520815
Sum5863
Variance710.7128785
MonotonicityNot monotonic
2022-09-04T23:41:53.017467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019
 
11.2%
1114
 
8.2%
411
 
6.5%
2311
 
6.5%
9210
 
5.9%
479
 
5.3%
178
 
4.7%
537
 
4.1%
306
 
3.5%
915
 
2.9%
Other values (42)70
41.2%
ValueCountFrequency (%)
34
 
2.4%
411
6.5%
61
 
0.6%
72
 
1.2%
81
 
0.6%
93
 
1.8%
1114
8.2%
142
 
1.2%
162
 
1.2%
178
4.7%
ValueCountFrequency (%)
1001
 
0.6%
971
 
0.6%
941
 
0.6%
9210
5.9%
915
2.9%
811
 
0.6%
791
 
0.6%
771
 
0.6%
751
 
0.6%
741
 
0.6%

_embedded.show.network
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing170
Missing (%)100.0%
Memory size1.5 KiB

_embedded.show.webChannel.id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct41
Distinct (%)24.4%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean148.875
Minimum1
Maximum516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:53.108642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q121
median99
Q3287
95-th percentile396
Maximum516
Range515
Interquartile range (IQR)266

Descriptive statistics

Standard deviation150.4067215
Coefficient of variation (CV)1.010288642
Kurtosis-0.9029140381
Mean148.875
Median Absolute Deviation (MAD)96
Skewness0.7053313808
Sum25011
Variance22622.18189
MonotonicityNot monotonic
2022-09-04T23:41:53.197642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2122
12.9%
120
11.8%
35812
 
7.1%
11812
 
7.1%
312
 
7.1%
28711
 
6.5%
3110
 
5.9%
3968
 
4.7%
2388
 
4.7%
677
 
4.1%
Other values (31)46
27.1%
ValueCountFrequency (%)
120
11.8%
312
7.1%
121
 
0.6%
151
 
0.6%
2122
12.9%
303
 
1.8%
3110
5.9%
512
 
1.2%
561
 
0.6%
677
 
4.1%
ValueCountFrequency (%)
5162
 
1.2%
4981
 
0.6%
4641
 
0.6%
4402
 
1.2%
4101
 
0.6%
4051
 
0.6%
3968
4.7%
3671
 
0.6%
3651
 
0.6%
35812
7.1%

_embedded.show.webChannel.name
Categorical

HIGH CORRELATION
MISSING

Distinct41
Distinct (%)24.4%
Missing2
Missing (%)1.2%
Memory size1.5 KiB
YouTube
22 
Netflix
20 
ZEE5
12 
Youku
12 
Prime Video
12 
Other values (36)
90 

Length

Max length15
Median length14
Mean length7.035714286
Min length3

Characters and Unicode

Total characters1182
Distinct characters51
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)11.9%

Sample

1st rowSeasonvar
2nd rowivi
3rd rowPremier
4th rowivi
5th rowBilibili

Common Values

ValueCountFrequency (%)
YouTube22
12.9%
Netflix20
11.8%
ZEE512
 
7.1%
Youku12
 
7.1%
Prime Video12
 
7.1%
Disney+11
 
6.5%
Vimeo10
 
5.9%
Hoichoi8
 
4.7%
NRK TV8
 
4.7%
iQIYI7
 
4.1%
Other values (31)46
27.1%

Length

2022-09-04T23:41:53.285642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
youtube22
 
10.6%
netflix20
 
9.6%
zee512
 
5.8%
youku12
 
5.8%
prime12
 
5.8%
video12
 
5.8%
disney11
 
5.3%
vimeo10
 
4.8%
tv9
 
4.3%
nrk8
 
3.8%
Other values (43)80
38.5%

Most occurring characters

ValueCountFrequency (%)
e124
 
10.5%
i116
 
9.8%
o91
 
7.7%
u71
 
6.0%
T46
 
3.9%
V43
 
3.6%
Y41
 
3.5%
t40
 
3.4%
40
 
3.4%
N34
 
2.9%
Other values (41)536
45.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter781
66.1%
Uppercase Letter330
27.9%
Space Separator40
 
3.4%
Math Symbol17
 
1.4%
Decimal Number13
 
1.1%
Other Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e124
15.9%
i116
14.9%
o91
11.7%
u71
 
9.1%
t40
 
5.1%
l33
 
4.2%
r30
 
3.8%
b27
 
3.5%
n27
 
3.5%
m26
 
3.3%
Other values (13)196
25.1%
Uppercase Letter
ValueCountFrequency (%)
T46
13.9%
V43
13.0%
Y41
12.4%
N34
10.3%
E25
 
7.6%
Q17
 
5.2%
I17
 
5.2%
P17
 
5.2%
Z12
 
3.6%
D12
 
3.6%
Other values (12)66
20.0%
Math Symbol
ValueCountFrequency (%)
+15
88.2%
|2
 
11.8%
Decimal Number
ValueCountFrequency (%)
512
92.3%
21
 
7.7%
Space Separator
ValueCountFrequency (%)
40
100.0%
Other Punctuation
ValueCountFrequency (%)
:1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1111
94.0%
Common71
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e124
 
11.2%
i116
 
10.4%
o91
 
8.2%
u71
 
6.4%
T46
 
4.1%
V43
 
3.9%
Y41
 
3.7%
t40
 
3.6%
N34
 
3.1%
l33
 
3.0%
Other values (35)472
42.5%
Common
ValueCountFrequency (%)
40
56.3%
+15
 
21.1%
512
 
16.9%
|2
 
2.8%
:1
 
1.4%
21
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e124
 
10.5%
i116
 
9.8%
o91
 
7.7%
u71
 
6.0%
T46
 
3.9%
V43
 
3.6%
Y41
 
3.5%
t40
 
3.4%
40
 
3.4%
N34
 
2.9%
Other values (41)536
45.3%

_embedded.show.webChannel.country.name
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)19.7%
Missing94
Missing (%)55.3%
Memory size1.5 KiB
India
21 
China
19 
Norway
10 
Russian Federation
United States
Other values (10)
16 

Length

Max length18
Median length5
Mean length7.328947368
Min length5

Characters and Unicode

Total characters557
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)6.6%

Sample

1st rowRussian Federation
2nd rowRussian Federation
3rd rowRussian Federation
4th rowRussian Federation
5th rowChina

Common Values

ValueCountFrequency (%)
India21
 
12.4%
China19
 
11.2%
Norway10
 
5.9%
Russian Federation5
 
2.9%
United States5
 
2.9%
Korea, Republic of3
 
1.8%
Spain2
 
1.2%
Sweden2
 
1.2%
Portugal2
 
1.2%
Brazil2
 
1.2%
Other values (5)5
 
2.9%
(Missing)94
55.3%

Length

2022-09-04T23:41:53.480389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
india21
22.8%
china19
20.7%
norway10
10.9%
russian5
 
5.4%
federation5
 
5.4%
united5
 
5.4%
states5
 
5.4%
republic3
 
3.3%
of3
 
3.3%
korea3
 
3.3%
Other values (9)13
14.1%

Most occurring characters

ValueCountFrequency (%)
a81
14.5%
i64
 
11.5%
n61
 
11.0%
d35
 
6.3%
e34
 
6.1%
r24
 
4.3%
t23
 
4.1%
o23
 
4.1%
I21
 
3.8%
h20
 
3.6%
Other values (26)171
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter449
80.6%
Uppercase Letter89
 
16.0%
Space Separator16
 
2.9%
Other Punctuation3
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a81
18.0%
i64
14.3%
n61
13.6%
d35
7.8%
e34
7.6%
r24
 
5.3%
t23
 
5.1%
o23
 
5.1%
h20
 
4.5%
s17
 
3.8%
Other values (12)67
14.9%
Uppercase Letter
ValueCountFrequency (%)
I21
23.6%
C20
22.5%
N11
12.4%
S9
10.1%
R8
 
9.0%
F5
 
5.6%
U5
 
5.6%
B3
 
3.4%
K3
 
3.4%
P2
 
2.2%
Other values (2)2
 
2.2%
Space Separator
ValueCountFrequency (%)
16
100.0%
Other Punctuation
ValueCountFrequency (%)
,3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin538
96.6%
Common19
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a81
15.1%
i64
11.9%
n61
11.3%
d35
 
6.5%
e34
 
6.3%
r24
 
4.5%
t23
 
4.3%
o23
 
4.3%
I21
 
3.9%
h20
 
3.7%
Other values (24)152
28.3%
Common
ValueCountFrequency (%)
16
84.2%
,3
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a81
14.5%
i64
 
11.5%
n61
 
11.0%
d35
 
6.3%
e34
 
6.1%
r24
 
4.3%
t23
 
4.1%
o23
 
4.1%
I21
 
3.8%
h20
 
3.6%
Other values (26)171
30.7%

_embedded.show.webChannel.country.code
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)19.7%
Missing94
Missing (%)55.3%
Memory size1.5 KiB
IN
21 
CN
19 
NO
10 
RU
US
Other values (10)
16 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters152
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)6.6%

Sample

1st rowRU
2nd rowRU
3rd rowRU
4th rowRU
5th rowCN

Common Values

ValueCountFrequency (%)
IN21
 
12.4%
CN19
 
11.2%
NO10
 
5.9%
RU5
 
2.9%
US5
 
2.9%
KR3
 
1.8%
ES2
 
1.2%
SE2
 
1.2%
PT2
 
1.2%
BR2
 
1.2%
Other values (5)5
 
2.9%
(Missing)94
55.3%

Length

2022-09-04T23:41:53.557665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in21
27.6%
cn19
25.0%
no10
13.2%
ru5
 
6.6%
us5
 
6.6%
kr3
 
3.9%
es2
 
2.6%
se2
 
2.6%
pt2
 
2.6%
br2
 
2.6%
Other values (5)5
 
6.6%

Most occurring characters

ValueCountFrequency (%)
N51
33.6%
I21
13.8%
C20
 
13.2%
R11
 
7.2%
O10
 
6.6%
U10
 
6.6%
S9
 
5.9%
E5
 
3.3%
K3
 
2.0%
T3
 
2.0%
Other values (6)9
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter152
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N51
33.6%
I21
13.8%
C20
 
13.2%
R11
 
7.2%
O10
 
6.6%
U10
 
6.6%
S9
 
5.9%
E5
 
3.3%
K3
 
2.0%
T3
 
2.0%
Other values (6)9
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin152
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N51
33.6%
I21
13.8%
C20
 
13.2%
R11
 
7.2%
O10
 
6.6%
U10
 
6.6%
S9
 
5.9%
E5
 
3.3%
K3
 
2.0%
T3
 
2.0%
Other values (6)9
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N51
33.6%
I21
13.8%
C20
 
13.2%
R11
 
7.2%
O10
 
6.6%
U10
 
6.6%
S9
 
5.9%
E5
 
3.3%
K3
 
2.0%
T3
 
2.0%
Other values (6)9
 
5.9%

_embedded.show.webChannel.country.timezone
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)19.7%
Missing94
Missing (%)55.3%
Memory size1.5 KiB
Asia/Kolkata
21 
Asia/Shanghai
19 
Europe/Oslo
10 
Asia/Kamchatka
America/New_York
Other values (10)
16 

Length

Max length16
Median length15
Mean length12.84210526
Min length10

Characters and Unicode

Total characters976
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)6.6%

Sample

1st rowAsia/Kamchatka
2nd rowAsia/Kamchatka
3rd rowAsia/Kamchatka
4th rowAsia/Kamchatka
5th rowAsia/Shanghai

Common Values

ValueCountFrequency (%)
Asia/Kolkata21
 
12.4%
Asia/Shanghai19
 
11.2%
Europe/Oslo10
 
5.9%
Asia/Kamchatka5
 
2.9%
America/New_York5
 
2.9%
Asia/Seoul3
 
1.8%
Europe/Madrid2
 
1.2%
Europe/Stockholm2
 
1.2%
Europe/Lisbon2
 
1.2%
America/Noronha2
 
1.2%
Other values (5)5
 
2.9%
(Missing)94
55.3%

Length

2022-09-04T23:41:53.634796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
asia/kolkata21
27.6%
asia/shanghai19
25.0%
europe/oslo10
13.2%
asia/kamchatka5
 
6.6%
america/new_york5
 
6.6%
asia/seoul3
 
3.9%
europe/madrid2
 
2.6%
europe/stockholm2
 
2.6%
europe/lisbon2
 
2.6%
america/noronha2
 
2.6%
Other values (5)5
 
6.6%

Most occurring characters

ValueCountFrequency (%)
a160
16.4%
i82
 
8.4%
/76
 
7.8%
o68
 
7.0%
s66
 
6.8%
A58
 
5.9%
h48
 
4.9%
l39
 
4.0%
r38
 
3.9%
e37
 
3.8%
Other values (25)304
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter738
75.6%
Uppercase Letter157
 
16.1%
Other Punctuation76
 
7.8%
Connector Punctuation5
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a160
21.7%
i82
11.1%
o68
9.2%
s66
8.9%
h48
 
6.5%
l39
 
5.3%
r38
 
5.1%
e37
 
5.0%
k33
 
4.5%
t30
 
4.1%
Other values (11)137
18.6%
Uppercase Letter
ValueCountFrequency (%)
A58
36.9%
K27
17.2%
S24
15.3%
E19
 
12.1%
O10
 
6.4%
N7
 
4.5%
Y5
 
3.2%
M2
 
1.3%
L2
 
1.3%
I1
 
0.6%
Other values (2)2
 
1.3%
Other Punctuation
ValueCountFrequency (%)
/76
100.0%
Connector Punctuation
ValueCountFrequency (%)
_5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin895
91.7%
Common81
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a160
17.9%
i82
 
9.2%
o68
 
7.6%
s66
 
7.4%
A58
 
6.5%
h48
 
5.4%
l39
 
4.4%
r38
 
4.2%
e37
 
4.1%
k33
 
3.7%
Other values (23)266
29.7%
Common
ValueCountFrequency (%)
/76
93.8%
_5
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a160
16.4%
i82
 
8.4%
/76
 
7.8%
o68
 
7.0%
s66
 
6.8%
A58
 
5.9%
h48
 
4.9%
l39
 
4.0%
r38
 
3.9%
e37
 
3.8%
Other values (25)304
31.1%

_embedded.show.webChannel.officialSite
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)16.5%
Missing79
Missing (%)46.5%
Memory size1.5 KiB
https://www.youtube.com
22 
https://www.netflix.com/
20 
https://www.primevideo.com
12 
https://www.disneyplus.com/
11 
https://www.iq.com/
Other values (10)
19 

Length

Max length30
Median length26
Mean length23.42857143
Min length17

Characters and Unicode

Total characters2132
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)5.5%

Sample

1st rowhttps://www.ivi.ru/
2nd rowhttps://www.ivi.ru/
3rd rowhttps://v.qq.com/
4th rowhttps://v.qq.com/
5th rowhttps://v.qq.com/

Common Values

ValueCountFrequency (%)
https://www.youtube.com22
 
12.9%
https://www.netflix.com/20
 
11.8%
https://www.primevideo.com12
 
7.1%
https://www.disneyplus.com/11
 
6.5%
https://www.iq.com/7
 
4.1%
https://v.qq.com/5
 
2.9%
https://tv.naver.com/3
 
1.8%
https://www.ivi.ru/2
 
1.2%
https://www.discoveryplus.com/2
 
1.2%
https://www.viki.com/2
 
1.2%
Other values (5)5
 
2.9%
(Missing)79
46.5%

Length

2022-09-04T23:41:53.717322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.youtube.com22
24.2%
https://www.netflix.com20
22.0%
https://www.primevideo.com12
13.2%
https://www.disneyplus.com11
12.1%
https://www.iq.com7
 
7.7%
https://v.qq.com5
 
5.5%
https://tv.naver.com3
 
3.3%
https://www.ivi.ru2
 
2.2%
https://www.discoveryplus.com2
 
2.2%
https://www.viki.com2
 
2.2%
Other values (5)5
 
5.5%

Most occurring characters

ValueCountFrequency (%)
w248
11.6%
/237
11.1%
t231
 
10.8%
.181
 
8.5%
o127
 
6.0%
s119
 
5.6%
p118
 
5.5%
m100
 
4.7%
h92
 
4.3%
c92
 
4.3%
Other values (16)587
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1623
76.1%
Other Punctuation509
 
23.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w248
15.3%
t231
14.2%
o127
 
7.8%
s119
 
7.3%
p118
 
7.3%
m100
 
6.2%
h92
 
5.7%
c92
 
5.7%
e87
 
5.4%
i75
 
4.6%
Other values (13)334
20.6%
Other Punctuation
ValueCountFrequency (%)
/237
46.6%
.181
35.6%
:91
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1623
76.1%
Common509
 
23.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
w248
15.3%
t231
14.2%
o127
 
7.8%
s119
 
7.3%
p118
 
7.3%
m100
 
6.2%
h92
 
5.7%
c92
 
5.7%
e87
 
5.4%
i75
 
4.6%
Other values (13)334
20.6%
Common
ValueCountFrequency (%)
/237
46.6%
.181
35.6%
:91
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w248
11.6%
/237
11.1%
t231
 
10.8%
.181
 
8.5%
o127
 
6.0%
s119
 
5.6%
p118
 
5.5%
m100
 
4.7%
h92
 
4.3%
c92
 
4.3%
Other values (16)587
27.5%

_embedded.show.dvdCountry
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing170
Missing (%)100.0%
Memory size1.5 KiB

_embedded.show.externals.tvrage
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)50.0%
Missing168
Missing (%)98.8%
Memory size1.5 KiB
34149.0

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters14
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row34149.0
2nd row34149.0

Common Values

ValueCountFrequency (%)
34149.02
 
1.2%
(Missing)168
98.8%

Length

2022-09-04T23:41:53.797470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:53.866477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
34149.02
100.0%

Most occurring characters

ValueCountFrequency (%)
44
28.6%
32
14.3%
12
14.3%
92
14.3%
.2
14.3%
02
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12
85.7%
Other Punctuation2
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
44
33.3%
32
16.7%
12
16.7%
92
16.7%
02
16.7%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common14
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
44
28.6%
32
14.3%
12
14.3%
92
14.3%
.2
14.3%
02
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII14
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
28.6%
32
14.3%
12
14.3%
92
14.3%
.2
14.3%
02
14.3%

_embedded.show.externals.thetvdb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct64
Distinct (%)48.1%
Missing37
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean368790.5188
Minimum257720
Maximum411923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:53.930738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum257720
5-th percentile294670.6
Q1364865
median388066
Q3393571
95-th percentile393732.2
Maximum411923
Range154203
Interquartile range (IQR)28706

Descriptive statistics

Standard deviation37252.10179
Coefficient of variation (CV)0.1010115496
Kurtosis1.087324825
Mean368790.5188
Median Absolute Deviation (MAD)5659
Skewness-1.513368976
Sum49049139
Variance1387719088
MonotonicityNot monotonic
2022-09-04T23:41:54.017747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39357112
 
7.1%
29732710
 
5.9%
37438910
 
5.9%
3936538
 
4.7%
3936097
 
4.1%
3685976
 
3.5%
3916276
 
3.5%
3880665
 
2.9%
3937254
 
2.4%
3972472
 
1.2%
Other values (54)63
37.1%
(Missing)37
21.8%
ValueCountFrequency (%)
2577202
 
1.2%
2651931
 
0.6%
2721571
 
0.6%
2787932
 
1.2%
2906861
 
0.6%
29732710
5.9%
3103111
 
0.6%
3106061
 
0.6%
3219271
 
0.6%
3234201
 
0.6%
ValueCountFrequency (%)
4119231
 
0.6%
4047691
 
0.6%
3977341
 
0.6%
3972472
 
1.2%
3939421
 
0.6%
3937431
 
0.6%
3937254
 
2.4%
3936538
4.7%
3936097
4.1%
39357112
7.1%

_embedded.show.externals.imdb
Categorical

HIGH CORRELATION
MISSING

Distinct48
Distinct (%)40.7%
Missing52
Missing (%)30.6%
Memory size1.5 KiB
tt13032762
12 
tt11612120
10 
tt5000936
10 
tt13582638
tt13600152
 
7
Other values (43)
71 

Length

Max length10
Median length10
Mean length9.771186441
Min length9

Characters and Unicode

Total characters1153
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)25.4%

Sample

1st rowtt8871128
2nd rowtt8871128
3rd rowtt11347388
4th rowtt9272742
5th rowtt13452364

Common Values

ValueCountFrequency (%)
tt1303276212
 
7.1%
tt1161212010
 
5.9%
tt500093610
 
5.9%
tt135826388
 
4.7%
tt136001527
 
4.1%
tt110975766
 
3.5%
tt100693986
 
3.5%
tt106896145
 
2.9%
tt122171524
 
2.4%
tt132069884
 
2.4%
Other values (38)46
27.1%
(Missing)52
30.6%

Length

2022-09-04T23:41:54.101991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tt1303276212
 
10.2%
tt500093610
 
8.5%
tt1161212010
 
8.5%
tt135826388
 
6.8%
tt136001527
 
5.9%
tt110975766
 
5.1%
tt100693986
 
5.1%
tt106896145
 
4.2%
tt122171524
 
3.4%
tt132069884
 
3.4%
Other values (38)46
39.0%

Most occurring characters

ValueCountFrequency (%)
t236
20.5%
1176
15.3%
0132
11.4%
2118
10.2%
6102
8.8%
399
8.6%
878
 
6.8%
768
 
5.9%
957
 
4.9%
553
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number917
79.5%
Lowercase Letter236
 
20.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1176
19.2%
0132
14.4%
2118
12.9%
6102
11.1%
399
10.8%
878
8.5%
768
 
7.4%
957
 
6.2%
553
 
5.8%
434
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
t236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common917
79.5%
Latin236
 
20.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1176
19.2%
0132
14.4%
2118
12.9%
6102
11.1%
399
10.8%
878
8.5%
768
 
7.4%
957
 
6.2%
553
 
5.8%
434
 
3.7%
Latin
ValueCountFrequency (%)
t236
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1153
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t236
20.5%
1176
15.3%
0132
11.4%
2118
10.2%
6102
8.8%
399
8.6%
878
 
6.8%
768
 
5.9%
957
 
4.9%
553
 
4.6%

_embedded.show.image.medium
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct84
Distinct (%)51.2%
Missing6
Missing (%)3.5%
Memory size1.5 KiB
https://static.tvmaze.com/uploads/images/medium_portrait/290/725957.jpg
12 
https://static.tvmaze.com/uploads/images/medium_portrait/213/533327.jpg
 
10
https://static.tvmaze.com/uploads/images/medium_portrait/285/712969.jpg
 
10
https://static.tvmaze.com/uploads/images/medium_portrait/290/725802.jpg
 
8
https://static.tvmaze.com/uploads/images/medium_portrait/298/745684.jpg
 
7
Other values (79)
117 

Length

Max length72
Median length71
Mean length71
Min length70

Characters and Unicode

Total characters11644
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)36.6%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_portrait/294/735323.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/285/713441.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/267/668675.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/289/723328.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/73/183375.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_portrait/290/725957.jpg12
 
7.1%
https://static.tvmaze.com/uploads/images/medium_portrait/213/533327.jpg10
 
5.9%
https://static.tvmaze.com/uploads/images/medium_portrait/285/712969.jpg10
 
5.9%
https://static.tvmaze.com/uploads/images/medium_portrait/290/725802.jpg8
 
4.7%
https://static.tvmaze.com/uploads/images/medium_portrait/298/745684.jpg7
 
4.1%
https://static.tvmaze.com/uploads/images/medium_portrait/282/705718.jpg6
 
3.5%
https://static.tvmaze.com/uploads/images/medium_portrait/231/579601.jpg6
 
3.5%
https://static.tvmaze.com/uploads/images/medium_portrait/289/722651.jpg6
 
3.5%
https://static.tvmaze.com/uploads/images/medium_portrait/282/707025.jpg5
 
2.9%
https://static.tvmaze.com/uploads/images/medium_portrait/350/877136.jpg4
 
2.4%
Other values (74)90
52.9%
(Missing)6
 
3.5%

Length

2022-09-04T23:41:54.175023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_portrait/290/725957.jpg12
 
7.3%
https://static.tvmaze.com/uploads/images/medium_portrait/213/533327.jpg10
 
6.1%
https://static.tvmaze.com/uploads/images/medium_portrait/285/712969.jpg10
 
6.1%
https://static.tvmaze.com/uploads/images/medium_portrait/290/725802.jpg8
 
4.9%
https://static.tvmaze.com/uploads/images/medium_portrait/298/745684.jpg7
 
4.3%
https://static.tvmaze.com/uploads/images/medium_portrait/282/705718.jpg6
 
3.7%
https://static.tvmaze.com/uploads/images/medium_portrait/231/579601.jpg6
 
3.7%
https://static.tvmaze.com/uploads/images/medium_portrait/289/722651.jpg6
 
3.7%
https://static.tvmaze.com/uploads/images/medium_portrait/282/707025.jpg5
 
3.0%
https://static.tvmaze.com/uploads/images/medium_portrait/350/877136.jpg4
 
2.4%
Other values (74)90
54.9%

Most occurring characters

ValueCountFrequency (%)
t1148
 
9.9%
/1148
 
9.9%
m820
 
7.0%
a820
 
7.0%
p656
 
5.6%
s656
 
5.6%
i656
 
5.6%
.492
 
4.2%
o492
 
4.2%
e492
 
4.2%
Other values (22)4264
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8200
70.4%
Other Punctuation1804
 
15.5%
Decimal Number1476
 
12.7%
Connector Punctuation164
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1148
14.0%
m820
10.0%
a820
10.0%
p656
 
8.0%
s656
 
8.0%
i656
 
8.0%
o492
 
6.0%
e492
 
6.0%
u328
 
4.0%
d328
 
4.0%
Other values (8)1804
22.0%
Decimal Number
ValueCountFrequency (%)
2256
17.3%
7215
14.6%
8166
11.2%
5157
10.6%
1148
10.0%
9141
9.6%
3132
8.9%
0103
7.0%
687
 
5.9%
471
 
4.8%
Other Punctuation
ValueCountFrequency (%)
/1148
63.6%
.492
27.3%
:164
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8200
70.4%
Common3444
29.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1148
14.0%
m820
10.0%
a820
10.0%
p656
 
8.0%
s656
 
8.0%
i656
 
8.0%
o492
 
6.0%
e492
 
6.0%
u328
 
4.0%
d328
 
4.0%
Other values (8)1804
22.0%
Common
ValueCountFrequency (%)
/1148
33.3%
.492
14.3%
2256
 
7.4%
7215
 
6.2%
8166
 
4.8%
_164
 
4.8%
:164
 
4.8%
5157
 
4.6%
1148
 
4.3%
9141
 
4.1%
Other values (4)393
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t1148
 
9.9%
/1148
 
9.9%
m820
 
7.0%
a820
 
7.0%
p656
 
5.6%
s656
 
5.6%
i656
 
5.6%
.492
 
4.2%
o492
 
4.2%
e492
 
4.2%
Other values (22)4264
36.6%

_embedded.show.image.original
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct84
Distinct (%)51.2%
Missing6
Missing (%)3.5%
Memory size1.5 KiB
https://static.tvmaze.com/uploads/images/original_untouched/290/725957.jpg
12 
https://static.tvmaze.com/uploads/images/original_untouched/213/533327.jpg
 
10
https://static.tvmaze.com/uploads/images/original_untouched/285/712969.jpg
 
10
https://static.tvmaze.com/uploads/images/original_untouched/290/725802.jpg
 
8
https://static.tvmaze.com/uploads/images/original_untouched/298/745684.jpg
 
7
Other values (79)
117 

Length

Max length75
Median length74
Mean length74
Min length73

Characters and Unicode

Total characters12136
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)36.6%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/294/735323.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/285/713441.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/267/668675.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/289/723328.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/73/183375.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/290/725957.jpg12
 
7.1%
https://static.tvmaze.com/uploads/images/original_untouched/213/533327.jpg10
 
5.9%
https://static.tvmaze.com/uploads/images/original_untouched/285/712969.jpg10
 
5.9%
https://static.tvmaze.com/uploads/images/original_untouched/290/725802.jpg8
 
4.7%
https://static.tvmaze.com/uploads/images/original_untouched/298/745684.jpg7
 
4.1%
https://static.tvmaze.com/uploads/images/original_untouched/282/705718.jpg6
 
3.5%
https://static.tvmaze.com/uploads/images/original_untouched/231/579601.jpg6
 
3.5%
https://static.tvmaze.com/uploads/images/original_untouched/289/722651.jpg6
 
3.5%
https://static.tvmaze.com/uploads/images/original_untouched/282/707025.jpg5
 
2.9%
https://static.tvmaze.com/uploads/images/original_untouched/350/877136.jpg4
 
2.4%
Other values (74)90
52.9%
(Missing)6
 
3.5%

Length

2022-09-04T23:41:54.257102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/290/725957.jpg12
 
7.3%
https://static.tvmaze.com/uploads/images/original_untouched/213/533327.jpg10
 
6.1%
https://static.tvmaze.com/uploads/images/original_untouched/285/712969.jpg10
 
6.1%
https://static.tvmaze.com/uploads/images/original_untouched/290/725802.jpg8
 
4.9%
https://static.tvmaze.com/uploads/images/original_untouched/298/745684.jpg7
 
4.3%
https://static.tvmaze.com/uploads/images/original_untouched/282/705718.jpg6
 
3.7%
https://static.tvmaze.com/uploads/images/original_untouched/231/579601.jpg6
 
3.7%
https://static.tvmaze.com/uploads/images/original_untouched/289/722651.jpg6
 
3.7%
https://static.tvmaze.com/uploads/images/original_untouched/282/707025.jpg5
 
3.0%
https://static.tvmaze.com/uploads/images/original_untouched/350/877136.jpg4
 
2.4%
Other values (74)90
54.9%

Most occurring characters

ValueCountFrequency (%)
/1148
 
9.5%
t984
 
8.1%
a820
 
6.8%
s656
 
5.4%
i656
 
5.4%
o656
 
5.4%
p492
 
4.1%
c492
 
4.1%
.492
 
4.1%
g492
 
4.1%
Other values (23)5248
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8692
71.6%
Other Punctuation1804
 
14.9%
Decimal Number1476
 
12.2%
Connector Punctuation164
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t984
 
11.3%
a820
 
9.4%
s656
 
7.5%
i656
 
7.5%
o656
 
7.5%
p492
 
5.7%
c492
 
5.7%
g492
 
5.7%
m492
 
5.7%
e492
 
5.7%
Other values (9)2460
28.3%
Decimal Number
ValueCountFrequency (%)
2256
17.3%
7215
14.6%
8166
11.2%
5157
10.6%
1148
10.0%
9141
9.6%
3132
8.9%
0103
7.0%
687
 
5.9%
471
 
4.8%
Other Punctuation
ValueCountFrequency (%)
/1148
63.6%
.492
27.3%
:164
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8692
71.6%
Common3444
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t984
 
11.3%
a820
 
9.4%
s656
 
7.5%
i656
 
7.5%
o656
 
7.5%
p492
 
5.7%
c492
 
5.7%
g492
 
5.7%
m492
 
5.7%
e492
 
5.7%
Other values (9)2460
28.3%
Common
ValueCountFrequency (%)
/1148
33.3%
.492
14.3%
2256
 
7.4%
7215
 
6.2%
8166
 
4.8%
:164
 
4.8%
_164
 
4.8%
5157
 
4.6%
1148
 
4.3%
9141
 
4.1%
Other values (4)393
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII12136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/1148
 
9.5%
t984
 
8.1%
a820
 
6.8%
s656
 
5.4%
i656
 
5.4%
o656
 
5.4%
p492
 
4.1%
c492
 
4.1%
.492
 
4.1%
g492
 
4.1%
Other values (23)5248
43.2%

_embedded.show.summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct82
Distinct (%)52.6%
Missing14
Missing (%)8.2%
Memory size1.5 KiB
<p>Veera, Jayati and Kavita, tired of abusive and unloving husbands, plan and execute the murder of their husbands in what they make look like a boat accident. It was an almost perfect plan, as the boat blew up mid sea, when the three couples had gone for a quick vacation, in what looked like a simple boating accident. It's a secret the three women share, a secret that will help them move on from the drudgery of their unhappy lives. How they get out and save themselves is what forms the spine of the series.</p>
12 
<p>Adapted from a popular webtoon of the same name, <b>Sweet Home</b> is a VFX/SFX filled thriller based on the unique world in which people turn into monsters that reflect their internal desires. Cha Hyun Soo, a reclusive high school student who moves into a new apartment called Green Home after a personal tragedy, faces a series of life changing situations that brings him out to the world to save others. </p>
 
10
<p>A web-series that tells the story of Mel Béart and Liz Malmo, two actresses who meet while shooting a short film and end up taking to their real lives the romantic relationship that they portray in fiction as Scarlet and Simone.</p>
 
10
<p>Freezer van driver Taqdeer spirals into a dark game of destiny after he finds the dead body of an unclaimed woman in his truck.</p>
 
8
<p>Johanne finds herself at 30 years old to be the only one among friends and family without a partner. The constant comments on her single life and society's expectations of the perfect family Christmas finally gets to her. Johanne starts a 24 day hunt for a partner to bring home for Christmas.</p>
 
6
Other values (77)
110 

Length

Max length1360
Median length584
Mean length370.8782051
Min length54

Characters and Unicode

Total characters57857
Distinct characters99
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)37.8%

Sample

1st row<p>Weekly videodaydzhest on site seasonvar.ru and creative team viruseproject.tv. In ten minutes, we talk about the most important events of the past week: look down on the set is not yet published projects, sharing the secrets of private life actors consider the prospects for the development of genres and discuss news TV industry! In videodaydzheste you will find only reliable information from Russian and foreign publications, as well as take part in choosing the best show of the month! Our weekly news videodaydzhest will suit every viewer, so gather good company with family and friends, as well as stock up on popcorn - these ten minutes you shock, delight and inform the latest news about your favorite TV projects!</p>
2nd row<p><b>Plague</b> – a Comedy project about how hard it is to survive in the middle ages during the plague. This is a story about the residents of the fictional town of Hamburg, locked in a castle under quarantine. Also locked up in the castle is the Messenger William, who, in fact, brought the news of the plague.</p>
3rd row<p>"To sho sketches" is an entertaining and unknowable show from the team of "Lena Kuka", in which there is no plot, logic, morality, and even more meaning. So turn off your brain and enjoy!</p>
4th row<p><b>Мужская тема</b> is a symbiosis of talk shows and modern podcasts, where male celebrities answer questions that concern people in the XXI century. Bright representatives of show business, theater, pop, cinema, sports, as well as Internet stars meet in the barbershop. Here, on male territory, they can openly discuss a variety of topics, sometimes seriously, and sometimes with humor. This is a chance to see the idol in a confidential communication without notes, compare his opinion with your own and hear what men really talk about when there is not a single girl around.</p>
5th row<p>Buy UCO from childhood grew up in the clan, Ichigo, but their "care" was for him a living Hell. Constant bullying, stealing the Goodies, without which Ycu can not live, and even eternal persecution, from the fair sex turned him into a goner cheapskate who wants to take revenge on his tormentors. But revenge is sweet and the path to it is thorny and to accomplish, UCU need to marry a girl Yes, as soon as possible. But when the heroes just happen? Never! And meeting with the small and the big-eared Fox Susan su su did not just destroy his plans, but starts spinning the wheel of fate that was waiting in the wings for hundreds of years!</p>

Common Values

ValueCountFrequency (%)
<p>Veera, Jayati and Kavita, tired of abusive and unloving husbands, plan and execute the murder of their husbands in what they make look like a boat accident. It was an almost perfect plan, as the boat blew up mid sea, when the three couples had gone for a quick vacation, in what looked like a simple boating accident. It's a secret the three women share, a secret that will help them move on from the drudgery of their unhappy lives. How they get out and save themselves is what forms the spine of the series.</p>12
 
7.1%
<p>Adapted from a popular webtoon of the same name, <b>Sweet Home</b> is a VFX/SFX filled thriller based on the unique world in which people turn into monsters that reflect their internal desires. Cha Hyun Soo, a reclusive high school student who moves into a new apartment called Green Home after a personal tragedy, faces a series of life changing situations that brings him out to the world to save others. </p>10
 
5.9%
<p>A web-series that tells the story of Mel Béart and Liz Malmo, two actresses who meet while shooting a short film and end up taking to their real lives the romantic relationship that they portray in fiction as Scarlet and Simone.</p>10
 
5.9%
<p>Freezer van driver Taqdeer spirals into a dark game of destiny after he finds the dead body of an unclaimed woman in his truck.</p>8
 
4.7%
<p>Johanne finds herself at 30 years old to be the only one among friends and family without a partner. The constant comments on her single life and society's expectations of the perfect family Christmas finally gets to her. Johanne starts a 24 day hunt for a partner to bring home for Christmas.</p>6
 
3.5%
<p><b>On Pointe</b> captures a season in the School of American Ballet (SAB) in New York City. Featuring unprecedented access to the famous ballet institution, the series follows the lives of the students ages 8 to 18 pursuing their dreams to become ballet dancers. While older students from all over the country rigorously train for professional careers, younger students from New York City are put through their paces as they rehearse and perform in New York City Ballet's holiday classic "George Balanchine's The Nutcracker" onstage at Lincoln Center.</p>6
 
3.5%
<p>Two boggling mysteries have occured in a small town in Xinan. A female police captain joins hands with a young detective to conduct an investigation. Although a clear motive can be seen, the two discover a series of unknown secrets.</p><p>One case involves a late-night ride hailed through an online platform that goes terribly wrong. As more and more clues resurface, the cases in the hands of the police hands become complicated and entangled. In a desperate attempt to find the real culprit, events closely link the past, present and future of the small town.</p>6
 
3.5%
<p><b>El Cid</b> retells from a contemporary perspective the story of the most famous Spaniard in history, a man trapped between two worlds and two cultures. A nobleman, a hero, a mercenary, a vassal, but also a man who could have been king. El Cid was centuries ahead of his time and became transcended by his own legend.</p>5
 
2.9%
<p>Welcome to <b>Bablo</b>, the world's best library!</p>4
 
2.4%
<p>At times dark, at times disturbing, four short films explore stories of those who dare to dream and desire — and those determined to stand in their way.</p>4
 
2.4%
Other values (72)85
50.0%
(Missing)14
 
8.2%

Length

2022-09-04T23:41:54.359310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the586
 
5.9%
a401
 
4.1%
and327
 
3.3%
of298
 
3.0%
to242
 
2.4%
in205
 
2.1%
that109
 
1.1%
their106
 
1.1%
his80
 
0.8%
is77
 
0.8%
Other values (1945)7469
75.4%

Most occurring characters

ValueCountFrequency (%)
9720
16.8%
e5579
 
9.6%
t3896
 
6.7%
a3605
 
6.2%
o3379
 
5.8%
n3144
 
5.4%
i2975
 
5.1%
s2889
 
5.0%
r2686
 
4.6%
h2356
 
4.1%
Other values (89)17628
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter43894
75.9%
Space Separator9749
 
16.9%
Other Punctuation1487
 
2.6%
Uppercase Letter1479
 
2.6%
Math Symbol989
 
1.7%
Decimal Number106
 
0.2%
Dash Punctuation87
 
0.2%
Format24
 
< 0.1%
Close Punctuation20
 
< 0.1%
Open Punctuation20
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5579
12.7%
t3896
 
8.9%
a3605
 
8.2%
o3379
 
7.7%
n3144
 
7.2%
i2975
 
6.8%
s2889
 
6.6%
r2686
 
6.1%
h2356
 
5.4%
l1863
 
4.2%
Other values (29)11522
26.2%
Uppercase Letter
ValueCountFrequency (%)
S172
 
11.6%
A125
 
8.5%
T119
 
8.0%
C118
 
8.0%
H77
 
5.2%
I75
 
5.1%
M71
 
4.8%
B69
 
4.7%
L62
 
4.2%
X57
 
3.9%
Other values (18)534
36.1%
Other Punctuation
ValueCountFrequency (%)
,539
36.2%
.458
30.8%
/269
18.1%
'109
 
7.3%
"60
 
4.0%
!23
 
1.5%
:17
 
1.1%
?7
 
0.5%
;4
 
0.3%
&1
 
0.1%
Decimal Number
ValueCountFrequency (%)
122
20.8%
219
17.9%
017
16.0%
816
15.1%
310
9.4%
47
 
6.6%
55
 
4.7%
95
 
4.7%
74
 
3.8%
61
 
0.9%
Math Symbol
ValueCountFrequency (%)
>494
49.9%
<494
49.9%
+1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-72
82.8%
11
 
12.6%
4
 
4.6%
Space Separator
ValueCountFrequency (%)
9720
99.7%
 29
 
0.3%
Format
ValueCountFrequency (%)
24
100.0%
Close Punctuation
ValueCountFrequency (%)
)20
100.0%
Open Punctuation
ValueCountFrequency (%)
(20
100.0%
Currency Symbol
ValueCountFrequency (%)
$2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin45362
78.4%
Common12484
 
21.6%
Cyrillic11
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5579
12.3%
t3896
 
8.6%
a3605
 
7.9%
o3379
 
7.4%
n3144
 
6.9%
i2975
 
6.6%
s2889
 
6.4%
r2686
 
5.9%
h2356
 
5.2%
l1863
 
4.1%
Other values (47)12990
28.6%
Common
ValueCountFrequency (%)
9720
77.9%
,539
 
4.3%
>494
 
4.0%
<494
 
4.0%
.458
 
3.7%
/269
 
2.2%
'109
 
0.9%
-72
 
0.6%
"60
 
0.5%
 29
 
0.2%
Other values (22)240
 
1.9%
Cyrillic
ValueCountFrequency (%)
а2
18.2%
т1
9.1%
м1
9.1%
е1
9.1%
с1
9.1%
я1
9.1%
к1
9.1%
ж1
9.1%
у1
9.1%
М1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII57761
99.8%
None46
 
0.1%
Punctuation39
 
0.1%
Cyrillic11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9720
16.8%
e5579
 
9.7%
t3896
 
6.7%
a3605
 
6.2%
o3379
 
5.8%
n3144
 
5.4%
i2975
 
5.2%
s2889
 
5.0%
r2686
 
4.7%
h2356
 
4.1%
Other values (70)17532
30.4%
None
ValueCountFrequency (%)
 29
63.0%
é10
 
21.7%
Í3
 
6.5%
ä2
 
4.3%
á1
 
2.2%
å1
 
2.2%
Punctuation
ValueCountFrequency (%)
24
61.5%
11
28.2%
4
 
10.3%
Cyrillic
ValueCountFrequency (%)
а2
18.2%
т1
9.1%
м1
9.1%
е1
9.1%
с1
9.1%
я1
9.1%
к1
9.1%
ж1
9.1%
у1
9.1%
М1
9.1%

_embedded.show.updated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1635447484
Minimum1604587145
Maximum1662290859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-04T23:41:54.460273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1604587145
5-th percentile1608690868
Q11617634625
median1636319538
Q31654593151
95-th percentile1661426439
Maximum1662290859
Range57703714
Interquartile range (IQR)36958526

Descriptive statistics

Standard deviation19743125.03
Coefficient of variation (CV)0.01207200184
Kurtosis-1.609278146
Mean1635447484
Median Absolute Deviation (MAD)18684913
Skewness-0.06946569614
Sum2.780260722 × 1011
Variance3.897909858 × 1014
MonotonicityNot monotonic
2022-09-04T23:41:54.553272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160876884612
 
7.1%
161763462510
 
5.9%
166142643910
 
5.9%
16086908688
 
4.7%
16250373767
 
4.1%
16545931516
 
3.5%
16134434036
 
3.5%
16498727206
 
3.5%
16268144425
 
2.9%
16342924674
 
2.4%
Other values (80)96
56.5%
ValueCountFrequency (%)
16045871451
 
0.6%
16078871751
 
0.6%
16084101481
 
0.6%
16086908688
4.7%
160876884612
7.1%
16094684031
 
0.6%
16095364481
 
0.6%
16096716402
 
1.2%
16103080041
 
0.6%
16113526801
 
0.6%
ValueCountFrequency (%)
16622908591
 
0.6%
16622799521
 
0.6%
16622629611
 
0.6%
16616900451
 
0.6%
16615373821
 
0.6%
16615205871
 
0.6%
166142643910
5.9%
16613636441
 
0.6%
16613408911
 
0.6%
16611987911
 
0.6%

_embedded.show._links.self.href
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct90
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
https://api.tvmaze.com/shows/52593
 
12
https://api.tvmaze.com/shows/44117
 
10
https://api.tvmaze.com/shows/46570
 
10
https://api.tvmaze.com/shows/52582
 
8
https://api.tvmaze.com/shows/53811
 
7
Other values (85)
123 

Length

Max length34
Median length34
Mean length33.99411765
Min length33

Characters and Unicode

Total characters5779
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)38.8%

Sample

1st rowhttps://api.tvmaze.com/shows/7847
2nd rowhttps://api.tvmaze.com/shows/48402
3rd rowhttps://api.tvmaze.com/shows/48403
4th rowhttps://api.tvmaze.com/shows/52520
5th rowhttps://api.tvmaze.com/shows/20734

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/shows/5259312
 
7.1%
https://api.tvmaze.com/shows/4411710
 
5.9%
https://api.tvmaze.com/shows/4657010
 
5.9%
https://api.tvmaze.com/shows/525828
 
4.7%
https://api.tvmaze.com/shows/538117
 
4.1%
https://api.tvmaze.com/shows/524516
 
3.5%
https://api.tvmaze.com/shows/414896
 
3.5%
https://api.tvmaze.com/shows/518366
 
3.5%
https://api.tvmaze.com/shows/410675
 
2.9%
https://api.tvmaze.com/shows/570294
 
2.4%
Other values (80)96
56.5%

Length

2022-09-04T23:41:54.649107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/shows/5259312
 
7.1%
https://api.tvmaze.com/shows/4411710
 
5.9%
https://api.tvmaze.com/shows/4657010
 
5.9%
https://api.tvmaze.com/shows/525828
 
4.7%
https://api.tvmaze.com/shows/538117
 
4.1%
https://api.tvmaze.com/shows/524516
 
3.5%
https://api.tvmaze.com/shows/414896
 
3.5%
https://api.tvmaze.com/shows/518366
 
3.5%
https://api.tvmaze.com/shows/410675
 
2.9%
https://api.tvmaze.com/shows/570294
 
2.4%
Other values (80)96
56.5%

Most occurring characters

ValueCountFrequency (%)
/680
 
11.8%
s510
 
8.8%
t510
 
8.8%
h340
 
5.9%
p340
 
5.9%
a340
 
5.9%
.340
 
5.9%
o340
 
5.9%
m340
 
5.9%
5171
 
3.0%
Other values (16)1868
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3740
64.7%
Other Punctuation1190
 
20.6%
Decimal Number849
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s510
13.6%
t510
13.6%
h340
9.1%
p340
9.1%
a340
9.1%
o340
9.1%
m340
9.1%
e170
 
4.5%
w170
 
4.5%
c170
 
4.5%
Other values (3)510
13.6%
Decimal Number
ValueCountFrequency (%)
5171
20.1%
1117
13.8%
4104
12.2%
299
11.7%
368
 
8.0%
766
 
7.8%
059
 
6.9%
657
 
6.7%
857
 
6.7%
951
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/680
57.1%
.340
28.6%
:170
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3740
64.7%
Common2039
35.3%

Most frequent character per script

Common
ValueCountFrequency (%)
/680
33.3%
.340
16.7%
5171
 
8.4%
:170
 
8.3%
1117
 
5.7%
4104
 
5.1%
299
 
4.9%
368
 
3.3%
766
 
3.2%
059
 
2.9%
Other values (3)165
 
8.1%
Latin
ValueCountFrequency (%)
s510
13.6%
t510
13.6%
h340
9.1%
p340
9.1%
a340
9.1%
o340
9.1%
m340
9.1%
e170
 
4.5%
w170
 
4.5%
c170
 
4.5%
Other values (3)510
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/680
 
11.8%
s510
 
8.8%
t510
 
8.8%
h340
 
5.9%
p340
 
5.9%
a340
 
5.9%
.340
 
5.9%
o340
 
5.9%
m340
 
5.9%
5171
 
3.0%
Other values (16)1868
32.3%

_embedded.show._links.previousepisode.href
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct90
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
https://api.tvmaze.com/episodes/1990515
 
12
https://api.tvmaze.com/episodes/2062416
 
10
https://api.tvmaze.com/episodes/1973995
 
10
https://api.tvmaze.com/episodes/1990243
 
8
https://api.tvmaze.com/episodes/2122277
 
7
Other values (85)
123 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters6630
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)38.8%

Sample

1st rowhttps://api.tvmaze.com/episodes/2338362
2nd rowhttps://api.tvmaze.com/episodes/1961005
3rd rowhttps://api.tvmaze.com/episodes/2044955
4th rowhttps://api.tvmaze.com/episodes/1988016
5th rowhttps://api.tvmaze.com/episodes/2153563

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/199051512
 
7.1%
https://api.tvmaze.com/episodes/206241610
 
5.9%
https://api.tvmaze.com/episodes/197399510
 
5.9%
https://api.tvmaze.com/episodes/19902438
 
4.7%
https://api.tvmaze.com/episodes/21222777
 
4.1%
https://api.tvmaze.com/episodes/19861746
 
3.5%
https://api.tvmaze.com/episodes/19855846
 
3.5%
https://api.tvmaze.com/episodes/19881246
 
3.5%
https://api.tvmaze.com/episodes/21313045
 
2.9%
https://api.tvmaze.com/episodes/21538674
 
2.4%
Other values (80)96
56.5%

Length

2022-09-04T23:41:54.726273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/199051512
 
7.1%
https://api.tvmaze.com/episodes/206241610
 
5.9%
https://api.tvmaze.com/episodes/197399510
 
5.9%
https://api.tvmaze.com/episodes/19902438
 
4.7%
https://api.tvmaze.com/episodes/21222777
 
4.1%
https://api.tvmaze.com/episodes/19861746
 
3.5%
https://api.tvmaze.com/episodes/19855846
 
3.5%
https://api.tvmaze.com/episodes/19881246
 
3.5%
https://api.tvmaze.com/episodes/21313045
 
2.9%
https://api.tvmaze.com/episodes/21538674
 
2.4%
Other values (80)96
56.5%

Most occurring characters

ValueCountFrequency (%)
/680
 
10.3%
p510
 
7.7%
s510
 
7.7%
e510
 
7.7%
t510
 
7.7%
o340
 
5.1%
a340
 
5.1%
i340
 
5.1%
.340
 
5.1%
m340
 
5.1%
Other values (16)2210
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4250
64.1%
Other Punctuation1190
 
17.9%
Decimal Number1190
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p510
12.0%
s510
12.0%
e510
12.0%
t510
12.0%
o340
8.0%
a340
8.0%
i340
8.0%
m340
8.0%
h170
 
4.0%
d170
 
4.0%
Other values (3)510
12.0%
Decimal Number
ValueCountFrequency (%)
2194
16.3%
1185
15.5%
9169
14.2%
5114
9.6%
4110
9.2%
792
7.7%
090
7.6%
390
7.6%
885
7.1%
661
 
5.1%
Other Punctuation
ValueCountFrequency (%)
/680
57.1%
.340
28.6%
:170
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4250
64.1%
Common2380
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/680
28.6%
.340
14.3%
2194
 
8.2%
1185
 
7.8%
:170
 
7.1%
9169
 
7.1%
5114
 
4.8%
4110
 
4.6%
792
 
3.9%
090
 
3.8%
Other values (3)236
 
9.9%
Latin
ValueCountFrequency (%)
p510
12.0%
s510
12.0%
e510
12.0%
t510
12.0%
o340
8.0%
a340
8.0%
i340
8.0%
m340
8.0%
h170
 
4.0%
d170
 
4.0%
Other values (3)510
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/680
 
10.3%
p510
 
7.7%
s510
 
7.7%
e510
 
7.7%
t510
 
7.7%
o340
 
5.1%
a340
 
5.1%
i340
 
5.1%
.340
 
5.1%
m340
 
5.1%
Other values (16)2210
33.3%
Distinct2
Distinct (%)100.0%
Missing168
Missing (%)98.8%
Memory size1.5 KiB
https://api.tvmaze.com/episodes/2338363
https://api.tvmaze.com/episodes/2354255

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters78
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2338363
2nd rowhttps://api.tvmaze.com/episodes/2354255

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/23383631
 
0.6%
https://api.tvmaze.com/episodes/23542551
 
0.6%
(Missing)168
98.8%

Length

2022-09-04T23:41:54.802273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:54.880273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/23383631
50.0%
https://api.tvmaze.com/episodes/23542551
50.0%

Most occurring characters

ValueCountFrequency (%)
/8
 
10.3%
p6
 
7.7%
s6
 
7.7%
t6
 
7.7%
e6
 
7.7%
35
 
6.4%
a4
 
5.1%
i4
 
5.1%
.4
 
5.1%
m4
 
5.1%
Other values (12)25
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50
64.1%
Other Punctuation14
 
17.9%
Decimal Number14
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p6
12.0%
s6
12.0%
t6
12.0%
e6
12.0%
a4
8.0%
i4
8.0%
m4
8.0%
o4
8.0%
h2
 
4.0%
d2
 
4.0%
Other values (3)6
12.0%
Decimal Number
ValueCountFrequency (%)
35
35.7%
53
21.4%
23
21.4%
81
 
7.1%
61
 
7.1%
41
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/8
57.1%
.4
28.6%
:2
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin50
64.1%
Common28
35.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
p6
12.0%
s6
12.0%
t6
12.0%
e6
12.0%
a4
8.0%
i4
8.0%
m4
8.0%
o4
8.0%
h2
 
4.0%
d2
 
4.0%
Other values (3)6
12.0%
Common
ValueCountFrequency (%)
/8
28.6%
35
17.9%
.4
14.3%
53
 
10.7%
23
 
10.7%
:2
 
7.1%
81
 
3.6%
61
 
3.6%
41
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII78
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/8
 
10.3%
p6
 
7.7%
s6
 
7.7%
t6
 
7.7%
e6
 
7.7%
35
 
6.4%
a4
 
5.1%
i4
 
5.1%
.4
 
5.1%
m4
 
5.1%
Other values (12)25
32.1%

image.medium
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct79
Distinct (%)100.0%
Missing91
Missing (%)53.5%
Memory size1.5 KiB
https://static.tvmaze.com/uploads/images/medium_landscape/295/737762.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/293/732707.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/293/732706.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/291/727776.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/291/728773.jpg
 
1
Other values (74)
74 

Length

Max length72
Median length72
Mean length72
Min length72

Characters and Unicode

Total characters5688
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_landscape/295/737762.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/289/724059.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/285/714187.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/290/726352.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/290/726073.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/295/737762.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/medium_landscape/293/732707.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/medium_landscape/293/732706.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/medium_landscape/291/727776.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/medium_landscape/291/728773.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/medium_landscape/291/728809.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/medium_landscape/289/723581.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/medium_landscape/290/725568.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/medium_landscape/290/725564.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/medium_landscape/291/729311.jpg1
 
0.6%
Other values (69)69
40.6%
(Missing)91
53.5%

Length

2022-09-04T23:41:54.954273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/295/737762.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/medium_landscape/289/723481.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/medium_landscape/289/724059.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/medium_landscape/285/714187.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726352.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726073.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726074.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726075.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726076.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/726077.jpg1
 
1.3%
Other values (69)69
87.3%

Most occurring characters

ValueCountFrequency (%)
/553
 
9.7%
a474
 
8.3%
m395
 
6.9%
s395
 
6.9%
t395
 
6.9%
p316
 
5.6%
e316
 
5.6%
.237
 
4.2%
d237
 
4.2%
c237
 
4.2%
Other values (22)2133
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4029
70.8%
Other Punctuation869
 
15.3%
Decimal Number711
 
12.5%
Connector Punctuation79
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a474
11.8%
m395
9.8%
s395
9.8%
t395
9.8%
p316
 
7.8%
e316
 
7.8%
d237
 
5.9%
c237
 
5.9%
i237
 
5.9%
g158
 
3.9%
Other values (8)869
21.6%
Decimal Number
ValueCountFrequency (%)
2163
22.9%
7109
15.3%
388
12.4%
984
11.8%
879
11.1%
060
 
8.4%
144
 
6.2%
632
 
4.5%
531
 
4.4%
421
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/553
63.6%
.237
27.3%
:79
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4029
70.8%
Common1659
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a474
11.8%
m395
9.8%
s395
9.8%
t395
9.8%
p316
 
7.8%
e316
 
7.8%
d237
 
5.9%
c237
 
5.9%
i237
 
5.9%
g158
 
3.9%
Other values (8)869
21.6%
Common
ValueCountFrequency (%)
/553
33.3%
.237
14.3%
2163
 
9.8%
7109
 
6.6%
388
 
5.3%
984
 
5.1%
879
 
4.8%
_79
 
4.8%
:79
 
4.8%
060
 
3.6%
Other values (4)128
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/553
 
9.7%
a474
 
8.3%
m395
 
6.9%
s395
 
6.9%
t395
 
6.9%
p316
 
5.6%
e316
 
5.6%
.237
 
4.2%
d237
 
4.2%
c237
 
4.2%
Other values (22)2133
37.5%

image.original
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct79
Distinct (%)100.0%
Missing91
Missing (%)53.5%
Memory size1.5 KiB
https://static.tvmaze.com/uploads/images/original_untouched/295/737762.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/293/732707.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/293/732706.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/291/727776.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/291/728773.jpg
 
1
Other values (74)
74 

Length

Max length74
Median length74
Mean length74
Min length74

Characters and Unicode

Total characters5846
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/295/737762.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/289/724059.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/285/714187.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/290/726352.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/290/726073.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/295/737762.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/original_untouched/293/732707.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/original_untouched/293/732706.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/original_untouched/291/727776.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/original_untouched/291/728773.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/original_untouched/291/728809.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/original_untouched/289/723581.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/original_untouched/290/725568.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/original_untouched/290/725564.jpg1
 
0.6%
https://static.tvmaze.com/uploads/images/original_untouched/291/729311.jpg1
 
0.6%
Other values (69)69
40.6%
(Missing)91
53.5%

Length

2022-09-04T23:41:55.030272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/295/737762.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/289/723481.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/289/724059.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/285/714187.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/726352.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/726073.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/726074.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/726075.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/726076.jpg1
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/726077.jpg1
 
1.3%
Other values (69)69
87.3%

Most occurring characters

ValueCountFrequency (%)
/553
 
9.5%
t474
 
8.1%
a395
 
6.8%
s316
 
5.4%
o316
 
5.4%
i316
 
5.4%
m237
 
4.1%
u237
 
4.1%
e237
 
4.1%
g237
 
4.1%
Other values (23)2528
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4187
71.6%
Other Punctuation869
 
14.9%
Decimal Number711
 
12.2%
Connector Punctuation79
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t474
 
11.3%
a395
 
9.4%
s316
 
7.5%
o316
 
7.5%
i316
 
7.5%
m237
 
5.7%
u237
 
5.7%
e237
 
5.7%
g237
 
5.7%
c237
 
5.7%
Other values (9)1185
28.3%
Decimal Number
ValueCountFrequency (%)
2163
22.9%
7109
15.3%
388
12.4%
984
11.8%
879
11.1%
060
 
8.4%
144
 
6.2%
632
 
4.5%
531
 
4.4%
421
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/553
63.6%
.237
27.3%
:79
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4187
71.6%
Common1659
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t474
 
11.3%
a395
 
9.4%
s316
 
7.5%
o316
 
7.5%
i316
 
7.5%
m237
 
5.7%
u237
 
5.7%
e237
 
5.7%
g237
 
5.7%
c237
 
5.7%
Other values (9)1185
28.3%
Common
ValueCountFrequency (%)
/553
33.3%
.237
14.3%
2163
 
9.8%
7109
 
6.6%
388
 
5.3%
984
 
5.1%
_79
 
4.8%
:79
 
4.8%
879
 
4.8%
060
 
3.6%
Other values (4)128
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/553
 
9.5%
t474
 
8.1%
a395
 
6.8%
s316
 
5.4%
o316
 
5.4%
i316
 
5.4%
m237
 
4.1%
u237
 
4.1%
e237
 
4.1%
g237
 
4.1%
Other values (23)2528
43.2%

_embedded.show.network.id
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing166
Missing (%)97.6%
Memory size1.5 KiB
339.0
374.0
78.0

Length

Max length5
Median length5
Mean length4.75
Min length4

Characters and Unicode

Total characters19
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row339.0
2nd row339.0
3rd row374.0
4th row78.0

Common Values

ValueCountFrequency (%)
339.02
 
1.2%
374.01
 
0.6%
78.01
 
0.6%
(Missing)166
97.6%

Length

2022-09-04T23:41:55.109272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:55.187569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
339.02
50.0%
374.01
25.0%
78.01
25.0%

Most occurring characters

ValueCountFrequency (%)
35
26.3%
.4
21.1%
04
21.1%
92
 
10.5%
72
 
10.5%
41
 
5.3%
81
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15
78.9%
Other Punctuation4
 
21.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
35
33.3%
04
26.7%
92
 
13.3%
72
 
13.3%
41
 
6.7%
81
 
6.7%
Other Punctuation
ValueCountFrequency (%)
.4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common19
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
35
26.3%
.4
21.1%
04
21.1%
92
 
10.5%
72
 
10.5%
41
 
5.3%
81
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII19
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35
26.3%
.4
21.1%
04
21.1%
92
 
10.5%
72
 
10.5%
41
 
5.3%
81
 
5.3%

_embedded.show.network.name
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing166
Missing (%)97.6%
Memory size1.5 KiB
TV 2
TV Globo
Disney Channel

Length

Max length14
Median length11
Mean length7.5
Min length4

Characters and Unicode

Total characters30
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowTV 2
2nd rowTV 2
3rd rowTV Globo
4th rowDisney Channel

Common Values

ValueCountFrequency (%)
TV 22
 
1.2%
TV Globo1
 
0.6%
Disney Channel1
 
0.6%
(Missing)166
97.6%

Length

2022-09-04T23:41:55.261568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:55.340568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
tv3
37.5%
22
25.0%
globo1
 
12.5%
disney1
 
12.5%
channel1
 
12.5%

Most occurring characters

ValueCountFrequency (%)
4
13.3%
T3
10.0%
n3
10.0%
V3
10.0%
22
 
6.7%
l2
 
6.7%
o2
 
6.7%
e2
 
6.7%
h1
 
3.3%
C1
 
3.3%
Other values (7)7
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15
50.0%
Uppercase Letter9
30.0%
Space Separator4
 
13.3%
Decimal Number2
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n3
20.0%
l2
13.3%
o2
13.3%
e2
13.3%
h1
 
6.7%
y1
 
6.7%
s1
 
6.7%
i1
 
6.7%
b1
 
6.7%
a1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
T3
33.3%
V3
33.3%
C1
 
11.1%
D1
 
11.1%
G1
 
11.1%
Space Separator
ValueCountFrequency (%)
4
100.0%
Decimal Number
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin24
80.0%
Common6
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T3
12.5%
n3
12.5%
V3
12.5%
l2
 
8.3%
o2
 
8.3%
e2
 
8.3%
h1
 
4.2%
C1
 
4.2%
y1
 
4.2%
D1
 
4.2%
Other values (5)5
20.8%
Common
ValueCountFrequency (%)
4
66.7%
22
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4
13.3%
T3
10.0%
n3
10.0%
V3
10.0%
22
 
6.7%
l2
 
6.7%
o2
 
6.7%
e2
 
6.7%
h1
 
3.3%
C1
 
3.3%
Other values (7)7
23.3%

_embedded.show.network.country.name
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing166
Missing (%)97.6%
Memory size1.5 KiB
Norway
Brazil
United States

Length

Max length13
Median length6
Mean length7.75
Min length6

Characters and Unicode

Total characters31
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowNorway
2nd rowNorway
3rd rowBrazil
4th rowUnited States

Common Values

ValueCountFrequency (%)
Norway2
 
1.2%
Brazil1
 
0.6%
United States1
 
0.6%
(Missing)166
97.6%

Length

2022-09-04T23:41:55.535796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:55.615796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
norway2
40.0%
brazil1
20.0%
united1
20.0%
states1
20.0%

Most occurring characters

ValueCountFrequency (%)
a4
12.9%
t3
 
9.7%
r3
 
9.7%
N2
 
6.5%
w2
 
6.5%
y2
 
6.5%
i2
 
6.5%
o2
 
6.5%
e2
 
6.5%
S1
 
3.2%
Other values (8)8
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25
80.6%
Uppercase Letter5
 
16.1%
Space Separator1
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4
16.0%
t3
12.0%
r3
12.0%
w2
8.0%
y2
8.0%
i2
8.0%
o2
8.0%
e2
8.0%
d1
 
4.0%
l1
 
4.0%
Other values (3)3
12.0%
Uppercase Letter
ValueCountFrequency (%)
N2
40.0%
S1
20.0%
U1
20.0%
B1
20.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin30
96.8%
Common1
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a4
13.3%
t3
10.0%
r3
10.0%
N2
 
6.7%
w2
 
6.7%
y2
 
6.7%
i2
 
6.7%
o2
 
6.7%
e2
 
6.7%
S1
 
3.3%
Other values (7)7
23.3%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a4
12.9%
t3
 
9.7%
r3
 
9.7%
N2
 
6.5%
w2
 
6.5%
y2
 
6.5%
i2
 
6.5%
o2
 
6.5%
e2
 
6.5%
S1
 
3.2%
Other values (8)8
25.8%

_embedded.show.network.country.code
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing166
Missing (%)97.6%
Memory size1.5 KiB
NO
BR
US

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowNO
2nd rowNO
3rd rowBR
4th rowUS

Common Values

ValueCountFrequency (%)
NO2
 
1.2%
BR1
 
0.6%
US1
 
0.6%
(Missing)166
97.6%

Length

2022-09-04T23:41:55.689796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:55.766077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no2
50.0%
br1
25.0%
us1
25.0%

Most occurring characters

ValueCountFrequency (%)
N2
25.0%
O2
25.0%
B1
12.5%
R1
12.5%
U1
12.5%
S1
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter8
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N2
25.0%
O2
25.0%
B1
12.5%
R1
12.5%
U1
12.5%
S1
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin8
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N2
25.0%
O2
25.0%
B1
12.5%
R1
12.5%
U1
12.5%
S1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N2
25.0%
O2
25.0%
B1
12.5%
R1
12.5%
U1
12.5%
S1
12.5%

_embedded.show.network.country.timezone
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing166
Missing (%)97.6%
Memory size1.5 KiB
Europe/Oslo
America/Noronha
America/New_York

Length

Max length16
Median length15.5
Mean length13.25
Min length11

Characters and Unicode

Total characters53
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowEurope/Oslo
2nd rowEurope/Oslo
3rd rowAmerica/Noronha
4th rowAmerica/New_York

Common Values

ValueCountFrequency (%)
Europe/Oslo2
 
1.2%
America/Noronha1
 
0.6%
America/New_York1
 
0.6%
(Missing)166
97.6%

Length

2022-09-04T23:41:55.840077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:55.920208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
europe/oslo2
50.0%
america/noronha1
25.0%
america/new_york1
25.0%

Most occurring characters

ValueCountFrequency (%)
o7
 
13.2%
r6
 
11.3%
e5
 
9.4%
/4
 
7.5%
a3
 
5.7%
E2
 
3.8%
u2
 
3.8%
N2
 
3.8%
c2
 
3.8%
i2
 
3.8%
Other values (12)18
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter39
73.6%
Uppercase Letter9
 
17.0%
Other Punctuation4
 
7.5%
Connector Punctuation1
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o7
17.9%
r6
15.4%
e5
12.8%
a3
7.7%
u2
 
5.1%
c2
 
5.1%
i2
 
5.1%
m2
 
5.1%
l2
 
5.1%
s2
 
5.1%
Other values (5)6
15.4%
Uppercase Letter
ValueCountFrequency (%)
E2
22.2%
N2
22.2%
A2
22.2%
O2
22.2%
Y1
11.1%
Other Punctuation
ValueCountFrequency (%)
/4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin48
90.6%
Common5
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o7
14.6%
r6
 
12.5%
e5
 
10.4%
a3
 
6.2%
E2
 
4.2%
u2
 
4.2%
N2
 
4.2%
c2
 
4.2%
i2
 
4.2%
m2
 
4.2%
Other values (10)15
31.2%
Common
ValueCountFrequency (%)
/4
80.0%
_1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII53
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o7
 
13.2%
r6
 
11.3%
e5
 
9.4%
/4
 
7.5%
a3
 
5.7%
E2
 
3.8%
u2
 
3.8%
N2
 
3.8%
c2
 
3.8%
i2
 
3.8%
Other values (12)18
34.0%

_embedded.show.network.officialSite
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing170
Missing (%)100.0%
Memory size1.5 KiB

_embedded.show.webChannel
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing170
Missing (%)100.0%
Memory size1.5 KiB

_embedded.show.image
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing170
Missing (%)100.0%
Memory size1.5 KiB

_embedded.show.webChannel.country
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing170
Missing (%)100.0%
Memory size1.5 KiB

_embedded.show.dvdCountry.name
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing169
Missing (%)99.4%
Memory size1.5 KiB
Russian Federation

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters18
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowRussian Federation

Common Values

ValueCountFrequency (%)
Russian Federation1
 
0.6%
(Missing)169
99.4%

Length

2022-09-04T23:41:55.992208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:56.059208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
russian1
50.0%
federation1
50.0%

Most occurring characters

ValueCountFrequency (%)
s2
11.1%
i2
11.1%
a2
11.1%
n2
11.1%
e2
11.1%
R1
 
5.6%
u1
 
5.6%
1
 
5.6%
F1
 
5.6%
d1
 
5.6%
Other values (3)3
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15
83.3%
Uppercase Letter2
 
11.1%
Space Separator1
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s2
13.3%
i2
13.3%
a2
13.3%
n2
13.3%
e2
13.3%
u1
6.7%
d1
6.7%
r1
6.7%
t1
6.7%
o1
6.7%
Uppercase Letter
ValueCountFrequency (%)
R1
50.0%
F1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17
94.4%
Common1
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s2
11.8%
i2
11.8%
a2
11.8%
n2
11.8%
e2
11.8%
R1
5.9%
u1
5.9%
F1
5.9%
d1
5.9%
r1
5.9%
Other values (2)2
11.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s2
11.1%
i2
11.1%
a2
11.1%
n2
11.1%
e2
11.1%
R1
 
5.6%
u1
 
5.6%
1
 
5.6%
F1
 
5.6%
d1
 
5.6%
Other values (3)3
16.7%

_embedded.show.dvdCountry.code
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing169
Missing (%)99.4%
Memory size1.5 KiB
RU

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowRU

Common Values

ValueCountFrequency (%)
RU1
 
0.6%
(Missing)169
99.4%

Length

2022-09-04T23:41:56.119208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:56.185208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ru1
100.0%

Most occurring characters

ValueCountFrequency (%)
R1
50.0%
U1
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R1
50.0%
U1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R1
50.0%
U1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R1
50.0%
U1
50.0%

_embedded.show.dvdCountry.timezone
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing169
Missing (%)99.4%
Memory size1.5 KiB
Asia/Kamchatka

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters14
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowAsia/Kamchatka

Common Values

ValueCountFrequency (%)
Asia/Kamchatka1
 
0.6%
(Missing)169
99.4%

Length

2022-09-04T23:41:56.243479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:41:56.312563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
asia/kamchatka1
100.0%

Most occurring characters

ValueCountFrequency (%)
a4
28.6%
A1
 
7.1%
s1
 
7.1%
i1
 
7.1%
/1
 
7.1%
K1
 
7.1%
m1
 
7.1%
c1
 
7.1%
h1
 
7.1%
t1
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11
78.6%
Uppercase Letter2
 
14.3%
Other Punctuation1
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4
36.4%
s1
 
9.1%
i1
 
9.1%
m1
 
9.1%
c1
 
9.1%
h1
 
9.1%
t1
 
9.1%
k1
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
A1
50.0%
K1
50.0%
Other Punctuation
ValueCountFrequency (%)
/1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13
92.9%
Common1
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a4
30.8%
A1
 
7.7%
s1
 
7.7%
i1
 
7.7%
K1
 
7.7%
m1
 
7.7%
c1
 
7.7%
h1
 
7.7%
t1
 
7.7%
k1
 
7.7%
Common
ValueCountFrequency (%)
/1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a4
28.6%
A1
 
7.1%
s1
 
7.1%
i1
 
7.1%
/1
 
7.1%
K1
 
7.1%
m1
 
7.1%
c1
 
7.1%
h1
 
7.1%
t1
 
7.1%

Interactions

2022-09-04T23:41:45.840820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:31.971389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.168905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.186208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.442726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.546702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.758218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.861851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.062325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.136381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.298490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.454371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:44.700769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.933358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.220477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.244965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.257361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.530931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.646600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.828141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.937852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.143324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.203308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.374489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.531569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:44.813925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.031175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.299896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.328074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.335529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.605236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.735889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.915210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.013920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.221326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.276678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.487479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.615062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:44.909742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.108175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.373130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.408310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.413956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.731394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.809242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.027787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.102142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.321452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.348132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.557850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.690985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.023856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.183175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.443249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.488497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.480013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.821910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.883394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.123927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.174298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.402459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.422141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.642869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.778053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.103018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.256175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.624436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.564496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.539168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.914299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.970605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.199861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.241496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.475461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.511847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.754606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.853065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.181309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.331628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.686750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.640665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.808520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.015148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.062171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.303928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.338789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.548451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.588266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.857514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.934061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.259239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.405771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.749756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.715852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.898294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.091799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.284735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.381927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.409790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.644564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.673434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.928728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:44.026054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.351574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.481772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.815750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.800703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.997440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.173561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.356810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.469931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.637620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.727878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.739440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.013061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:44.112714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.438811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.548772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.876681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.866703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.066523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.250665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.427893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.543859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.733618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.802810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.816758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.081069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:44.198005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.506808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.620772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:32.945937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.943940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.159016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.322741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.502067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.613929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.802700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.885885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.066098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.157570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:44.287227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.582899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.696091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.015320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.020060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.240070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.395100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.576144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.678929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.885619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:40.962814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.145163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.258841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:44.380545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.667075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:46.775091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:33.097836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:34.103208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:35.360591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:36.471279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:37.688146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:38.778926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:39.984256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:41.044895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:42.224799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:43.356214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:44.609771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:41:45.753054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-09-04T23:41:56.391721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-04T23:41:56.639763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-04T23:41:56.893348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-04T23:41:57.175046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-04T23:41:47.200527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-04T23:41:48.382386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-04T23:41:48.973320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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01968114https://www.tvmaze.com/episodes/1968114/po-sezonu-videodajdzest-seasonvar-6x51-vypusk-305Выпуск 305651.0regular2020-12-182020-12-18T00:00:00+00:009.0NaNNoneNaNhttps://api.tvmaze.com/episodes/19681147847https://www.tvmaze.com/shows/7847/po-sezonu-videodajdzest-seasonvarПо сезону. Видеодайджест SeasonvarTalk ShowRussian[]Running9.08.02015-02-13Nonehttp://seasonvar.ru/serial-11488-Po_sezonu_Videodajdzhest_Seasonvar.html[Friday]NaN57NaN56.0SeasonvarRussian FederationRUAsia/KamchatkaNoneNaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/294/735323.jpghttps://static.tvmaze.com/uploads/images/original_untouched/294/735323.jpg<p>Weekly videodaydzhest on site seasonvar.ru and creative team viruseproject.tv. In ten minutes, we talk about the most important events of the past week: look down on the set is not yet published projects, sharing the secrets of private life actors consider the prospects for the development of genres and discuss news TV industry! In videodaydzheste you will find only reliable information from Russian and foreign publications, as well as take part in choosing the best show of the month! Our weekly news videodaydzhest will suit every viewer, so gather good company with family and friends, as well as stock up on popcorn - these ten minutes you shock, delight and inform the latest news about your favorite TV projects!</p>1662290859https://api.tvmaze.com/shows/7847https://api.tvmaze.com/episodes/2338362https://api.tvmaze.com/episodes/2338363NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11961005https://www.tvmaze.com/episodes/1961005/cuma-2x08-seria-14Серия 1428.0regular2020-12-182020-12-18T00:00:00+00:0023.0NaNNoneNaNhttps://api.tvmaze.com/episodes/196100548402https://www.tvmaze.com/shows/48402/cumaЧума!ScriptedRussian[Comedy]Ended21.021.02020-05-292020-12-18https://www.ivi.ru/watch/chuma-2020[Friday]6.029NaN337.0iviRussian FederationRUAsia/Kamchatkahttps://www.ivi.ru/NaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/285/713441.jpghttps://static.tvmaze.com/uploads/images/original_untouched/285/713441.jpg<p><b>Plague</b> – a Comedy project about how hard it is to survive in the middle ages during the plague. This is a story about the residents of the fictional town of Hamburg, locked in a castle under quarantine. Also locked up in the castle is the Messenger William, who, in fact, brought the news of the plague.</p>1609468403https://api.tvmaze.com/shows/48402https://api.tvmaze.com/episodes/1961005NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21989253https://www.tvmaze.com/episodes/1989253/to-so-sketci-1x07-7-vypusk7 выпуск17.0regular2020-12-1812:002020-12-18T00:00:00+00:0023.0NaNNoneNaNhttps://api.tvmaze.com/episodes/198925348403https://www.tvmaze.com/shows/48403/to-so-sketciТо шо скетчиVarietyRussian[Comedy]Ended10.015.02020-05-252021-03-11https://premier.one/show/to-sho-sketchi[Monday]NaN4NaN281.0PremierRussian FederationRUAsia/KamchatkaNoneNaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/267/668675.jpghttps://static.tvmaze.com/uploads/images/original_untouched/267/668675.jpg<p>"To sho sketches" is an entertaining and unknowable show from the team of "Lena Kuka", in which there is no plot, logic, morality, and even more meaning. So turn off your brain and enjoy!</p>1644239174https://api.tvmaze.com/shows/48403https://api.tvmaze.com/episodes/2044955NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31988013https://www.tvmaze.com/episodes/1988013/muzskaa-tema-1x02-seria-2Серия 212.0regular2020-12-1812:002020-12-18T00:00:00+00:0029.0NaNNoneNaNhttps://api.tvmaze.com/episodes/198801352520https://www.tvmaze.com/shows/52520/muzskaa-temaМужская темаTalk ShowRussian[]Ended30.030.02020-12-172020-12-25https://www.ivi.ru/watch/muzhskaya-tema12:00[Monday, Tuesday, Wednesday, Thursday, Friday]NaN4NaN337.0iviRussian FederationRUAsia/Kamchatkahttps://www.ivi.ru/NaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/289/723328.jpghttps://static.tvmaze.com/uploads/images/original_untouched/289/723328.jpg<p><b>Мужская тема</b> is a symbiosis of talk shows and modern podcasts, where male celebrities answer questions that concern people in the XXI century. Bright representatives of show business, theater, pop, cinema, sports, as well as Internet stars meet in the barbershop. Here, on male territory, they can openly discuss a variety of topics, sometimes seriously, and sometimes with humor. This is a chance to see the idol in a confidential communication without notes, compare his opinion with your own and hear what men really talk about when there is not a single girl around.</p>1616722619https://api.tvmaze.com/shows/52520https://api.tvmaze.com/episodes/1988016NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
42030153https://www.tvmaze.com/episodes/2030153/fox-spirit-matchmaker-9x03-episode-124Episode 12493.0regular2020-12-182020-12-18T04:00:00+00:0010.0NaNNoneNaNhttps://api.tvmaze.com/episodes/203015320734https://www.tvmaze.com/shows/20734/fox-spirit-matchmakerFox Spirit MatchmakerAnimationChinese[Comedy, Anime, Fantasy, Romance]Running10.010.02015-06-26Nonehttp://www.bilibili.com/bangumi/%E7%8B%90%E5%A6%96%E5%B0%8F%E7%BA%A2%E5%A8%98/[Friday]NaN68NaN51.0BilibiliChinaCNAsia/ShanghaiNoneNaNNaN310311.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/73/183375.jpghttps://static.tvmaze.com/uploads/images/original_untouched/73/183375.jpg<p>Buy UCO from childhood grew up in the clan, Ichigo, but their "care" was for him a living Hell. Constant bullying, stealing the Goodies, without which Ycu can not live, and even eternal persecution, from the fair sex turned him into a goner cheapskate who wants to take revenge on his tormentors. But revenge is sweet and the path to it is thorny and to accomplish, UCU need to marry a girl Yes, as soon as possible. But when the heroes just happen? Never! And meeting with the small and the big-eared Fox Susan su su did not just destroy his plans, but starts spinning the wheel of fate that was waiting in the wings for hundreds of years!</p>1629636336https://api.tvmaze.com/shows/20734https://api.tvmaze.com/episodes/2153563NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
51972569https://www.tvmaze.com/episodes/1972569/the-wolf-1x27-episode-27Episode 27127.0regular2020-12-182020-12-18T04:00:00+00:0045.0NaNNoneNaNhttps://api.tvmaze.com/episodes/197256947912https://www.tvmaze.com/shows/47912/the-wolfThe WolfScriptedChinese[Drama, Romance, History]Ended45.045.02020-11-192021-01-04https://www.iqiyi.com/lib/m_213579814.html[]NaN38NaN118.0YoukuChinaCNAsia/ShanghaiNoneNaNNaN331095.0tt8871128https://static.tvmaze.com/uploads/images/medium_portrait/255/639532.jpghttps://static.tvmaze.com/uploads/images/original_untouched/255/639532.jpg<p>The story happens at the end of the Tang Dynasty, when Zhu Wen usurps the throne and establishes the Later Liang Dynasty, and he's known as Emperor Taizu. Ma Zhai Xing (Li Qin) is the daughter of an official and as a child, she befriends a young boy (Darren Wang) who lives in the mountain. One day when he saves a wolf, he accidentally falls over the cliff and is rescued by Zhu Wen. The authoritative figure adopts him as a godson and gives him the title Bo Wang. Ten years later, he saves the female lead per chance and finds her courage and intelligence resonant, and she likes that while he's in a position of power, he still has humility and kindness. She encourages him to fight for justice and he begins that journey by helping the people, stopping throne fights, etc. Even when they have conflicts, they will face those frankly. As they overcome obstacles and fight for justice, feelings deepen and they are able to reap their own happiness by each other's side.</p>1648217029https://api.tvmaze.com/shows/47912https://api.tvmaze.com/episodes/1972591NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
61972570https://www.tvmaze.com/episodes/1972570/the-wolf-1x28-episode-28Episode 28128.0regular2020-12-182020-12-18T04:00:00+00:0045.0NaNNoneNaNhttps://api.tvmaze.com/episodes/197257047912https://www.tvmaze.com/shows/47912/the-wolfThe WolfScriptedChinese[Drama, Romance, History]Ended45.045.02020-11-192021-01-04https://www.iqiyi.com/lib/m_213579814.html[]NaN38NaN118.0YoukuChinaCNAsia/ShanghaiNoneNaNNaN331095.0tt8871128https://static.tvmaze.com/uploads/images/medium_portrait/255/639532.jpghttps://static.tvmaze.com/uploads/images/original_untouched/255/639532.jpg<p>The story happens at the end of the Tang Dynasty, when Zhu Wen usurps the throne and establishes the Later Liang Dynasty, and he's known as Emperor Taizu. Ma Zhai Xing (Li Qin) is the daughter of an official and as a child, she befriends a young boy (Darren Wang) who lives in the mountain. One day when he saves a wolf, he accidentally falls over the cliff and is rescued by Zhu Wen. The authoritative figure adopts him as a godson and gives him the title Bo Wang. Ten years later, he saves the female lead per chance and finds her courage and intelligence resonant, and she likes that while he's in a position of power, he still has humility and kindness. She encourages him to fight for justice and he begins that journey by helping the people, stopping throne fights, etc. Even when they have conflicts, they will face those frankly. As they overcome obstacles and fight for justice, feelings deepen and they are able to reap their own happiness by each other's side.</p>1648217029https://api.tvmaze.com/shows/47912https://api.tvmaze.com/episodes/1972591NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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81998583https://www.tvmaze.com/episodes/1998583/mr-right-is-here-1x11-episode-11Episode 11111.0regular2020-12-182020-12-18T04:00:00+00:0045.0NaNNoneNaNhttps://api.tvmaze.com/episodes/199858352782https://www.tvmaze.com/shows/52782/mr-right-is-hereMr. Right is Here!ScriptedChinese[Drama, Comedy, Romance]Ended45.045.02020-12-102020-12-18None[Thursday, Friday, Saturday]NaN16NaN118.0YoukuChinaCNAsia/ShanghaiNoneNaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/291/729462.jpghttps://static.tvmaze.com/uploads/images/original_untouched/291/729462.jpg<p>‎The fashion company faced a crisis. Sun Chi, the young owner of the company, at a critical moment took over the management and became the new CEO. He promised his father that in three months he would be able to promote the project "promoting fashion" that will help to get out of the crisis. ‎ </p><p>‎Gio Intao, who wanted to be the queen of the fashion industry, by coincidence got into the company and became subordinate to the "devil", a young gene. Director Sun Chi.‎   </p><p>‎Sun Chi and Xiao Intao led a fashion company to resolve the crisis and open new markets, allowing Chinese fashion brands to enter the global market step by step. At the same time, they begin to feel each other.‎</p>1609671640https://api.tvmaze.com/shows/52782https://api.tvmaze.com/episodes/1998584NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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idurlnameseasonnumbertypeairdateairtimeairstampruntimeimagesummaryrating.average_links.self.href_embedded.show.id_embedded.show.url_embedded.show.name_embedded.show.type_embedded.show.language_embedded.show.genres_embedded.show.status_embedded.show.runtime_embedded.show.averageRuntime_embedded.show.premiered_embedded.show.ended_embedded.show.officialSite_embedded.show.schedule.time_embedded.show.schedule.days_embedded.show.rating.average_embedded.show.weight_embedded.show.network_embedded.show.webChannel.id_embedded.show.webChannel.name_embedded.show.webChannel.country.name_embedded.show.webChannel.country.code_embedded.show.webChannel.country.timezone_embedded.show.webChannel.officialSite_embedded.show.dvdCountry_embedded.show.externals.tvrage_embedded.show.externals.thetvdb_embedded.show.externals.imdb_embedded.show.image.medium_embedded.show.image.original_embedded.show.summary_embedded.show.updated_embedded.show._links.self.href_embedded.show._links.previousepisode.href_embedded.show._links.nextepisode.hrefimage.mediumimage.original_embedded.show.network.id_embedded.show.network.name_embedded.show.network.country.name_embedded.show.network.country.code_embedded.show.network.country.timezone_embedded.show.network.officialSite_embedded.show.webChannel_embedded.show.image_embedded.show.webChannel.country_embedded.show.dvdCountry.name_embedded.show.dvdCountry.code_embedded.show.dvdCountry.timezone
1601993457https://www.tvmaze.com/episodes/1993457/diario-de-um-confinado-2x07-episodio-especialEpisódio Especial27.0regular2020-12-182020-12-18T14:00:00+00:0010.0NaNNoneNaNhttps://api.tvmaze.com/episodes/199345749024https://www.tvmaze.com/shows/49024/diario-de-um-confinadoDiário de Um ConfinadoScriptedPortuguese[]Running10.010.02020-06-26Nonehttps://gshow.globo.com/series/diario-de-um-confinado/[]NaN4NaN131.0GloboplayBrazilBRAmerica/NoronhaNoneNaNNaN384566.0tt12595280https://static.tvmaze.com/uploads/images/medium_portrait/418/1045179.jpghttps://static.tvmaze.com/uploads/images/original_untouched/418/1045179.jpgNone1659724050https://api.tvmaze.com/shows/49024https://api.tvmaze.com/episodes/1993457NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1611984684https://www.tvmaze.com/episodes/1984684/sj-returns-4x84-episode-84Episode 84484.0regular2020-12-1800:002020-12-18T15:00:00+00:005.0NaNNoneNaNhttps://api.tvmaze.com/episodes/198468452250https://www.tvmaze.com/shows/52250/sj-returnsSJ ReturnsRealityKorean[]Running5.05.02017-10-09Nonehttps://tv.naver.com/sjreturns00:00[Monday, Wednesday, Friday]NaN14NaN30.0Naver TVCastKorea, Republic ofKRAsia/Seoulhttps://tv.naver.com/NaNNaN336628.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/286/716978.jpghttps://static.tvmaze.com/uploads/images/original_untouched/286/716978.jpg<p>This show will follow Super Junior's everyday life.</p>1613088348https://api.tvmaze.com/shows/52250https://api.tvmaze.com/episodes/2030085NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1621968563https://www.tvmaze.com/episodes/1968563/creepshow-s01-special-shapeshifters-anonymousShapeshifters Anonymous1NaNsignificant_special2020-12-182020-12-18T17:00:00+00:0060.0NaN<p>Fearing he is a murderer, an anxious man searches for answers for his "unique condition" from an unusual support group.</p>6.1https://api.tvmaze.com/episodes/196856337790https://www.tvmaze.com/shows/37790/creepshowCreepshowScriptedEnglish[Drama, Horror]Running30.025.02019-09-26Nonehttps://www.shudder.com/series/watch/creepshow/5138009[Thursday]6.197NaN213.0ShudderUnited StatesUSAmerica/New_YorkNoneNaNNaN364865.0tt8762206https://static.tvmaze.com/uploads/images/medium_portrait/355/887896.jpghttps://static.tvmaze.com/uploads/images/original_untouched/355/887896.jpg<p><b>Creepshow</b>, the anthology series based on the 1982 horror comedy classic, is still the most fun you'll ever have being scared! A comic book comes to life in a series of twelve vignettes over six episodes, exploring terrors from murder to the supernatural and unexplainable. Haunted dollhouses, werewolves, murderous goblins, villainous trick-or-treaters, the dead, and medical marvels are just a few of the things to watch out for in this new series. You never know what will be on the next page...</p>1645494266https://api.tvmaze.com/shows/37790https://api.tvmaze.com/episodes/2198910NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/289/723408.jpghttps://static.tvmaze.com/uploads/images/original_untouched/289/723408.jpgNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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1662341526https://www.tvmaze.com/episodes/2341526/trailer-park-boys-park-after-dark-2x30-alien-mindfuckAlien Mindfuck230.0regular2020-12-182020-12-18T17:00:00+00:0030.0NaN<p>Julian is worried about aliens - are they here, and are they trying to bang us with their minds?! Find out which celeb birthdays give Ricky an erectoroni, and the ultimate Italian piss-off walk. Also: Bubbles has a Criminal Mind!</p>NaNhttps://api.tvmaze.com/episodes/234152662418https://www.tvmaze.com/shows/62418/trailer-park-boys-park-after-darkTrailer Park Boys: Park After DarkTalk ShowEnglish[Comedy]Running30.030.02019-04-05Nonehttps://www.swearnet.com/shows/park-after-dark[Friday]NaN9NaN464.0SwearNetCanadaCAAmerica/Halifaxhttps://www.swearnet.comNaNNaNNaNtt12107408https://static.tvmaze.com/uploads/images/medium_portrait/412/1030402.jpghttps://static.tvmaze.com/uploads/images/original_untouched/412/1030402.jpg<p>Hang out with Ricky, Julian and Bubbles in Ricky's kitchen, smoking, drinking and talking about whatever the hell pops up in their fucked up brains.</p>1661520587https://api.tvmaze.com/shows/62418https://api.tvmaze.com/episodes/2380574NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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1691951741https://www.tvmaze.com/episodes/1951741/205-live-2020-12-18-205-live-212205 Live #212202051.0regular2020-12-1822:002020-12-19T03:00:00+00:0060.0NaNNoneNaNhttps://api.tvmaze.com/episodes/195174122536https://www.tvmaze.com/shows/22536/205-live205 LiveSportsEnglish[]Ended60.060.02016-11-292022-02-11https://www.wwe.com/shows/wwe-205-live22:00[Friday]NaN77NaN15.0WWE NetworkUnited StatesUSAmerica/New_YorkNoneNaNNaN323420.0tt6286394https://static.tvmaze.com/uploads/images/medium_portrait/96/240881.jpghttps://static.tvmaze.com/uploads/images/original_untouched/96/240881.jpg<p><i>WWE 205 Live</i>, also simply called <b>205 Live</b>, is a live professional wrestling WWE Network series produced by WWE, which exclusively features the promotion's cruiserweight division, wherein all participants are billed at a weight of 205 lbs. or less.</p><p>The final episode of the series aired on February 11, 2022. On February 18, it was replaced by <i>WWE NXT: Level Up</i>.</p>1649921818https://api.tvmaze.com/shows/22536https://api.tvmaze.com/episodes/2267545NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN